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JACIII

ESCI

Journal of Advanced Computational Intelligence and Intelligent Informatics

ISSN : 1343-0130(Print) / 1883-8014(Online)
DOI : 10.20965/jaciii.issn.1883-8014
Honorary Editor : Lotfi A. Zadeh (University of California)
Editors-in-Chief : Toshio Fukuda (Meijo University),
Kaoru Hirota (Beijing Institute of Technology; JSPS Beijing Office)

Indexed in ESCI, Scopus, Compendex (Ei)

Scopus
  • In Cooperation with :
  • International Fuzzy Systems Association (IFSA), Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT), Brazilian Society of Automatics (SBA), The Society of Instrument and Control Engineers (SICE), John von Neumann Computer Society (NJSZT), Vietnamese Fuzzy Systems Society (VFSS), Fuzzy Systems and Intelligent Technologies Research Society of Thailand (FIRST), Korean Institute of Intelligent Systems (KIIS), Taiwanese Association for Artificial Intelligence (TAAI)

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2017-03-17T01:49:53+0000

Vol.8 (2004)

No.6

(Nov)

Selected Papers from InTech'03

Selected Papers from InTech'03

: p. 565
Selected Papers from InTech’03
Pratit Santiprabhob, Nipon Theera-Umpon, and Sansanee Auephanwiriyakul

On behalf of the Fuzzy systems and Intelligent technologies Research Society of Thailand (FIRST), Chiang Mai University, and Assumption University of Thailand, which have jointly organized the 4th International Conference on Intelligent Technology (InTech), we thank the editorial board of the Journal of Advanced Computational Intelligence and Intelligent Informatics for devoting part of this issue to selected papers from the Conference. The 4th InTech was held during December 17-19, 2003, in the lovely northern Thai city of Chiang Mai, becoming the most successful InTech ever, with over 110 papers accepted after stringent reviews for presentation at the Conference. Papers selected for publication in this special issue have been revised by their authors based on Journal guidelines. These papers detail a variety of applications of different intelligent technologies, ranging from data classification, system identification, and signal processing to full-text search and robotics. The technologies themselves are also varied, including fuzzy logic, genetic algorithms, regression, and data classification. We thank our reviewers, who have worked tirelessly in reviewing papers for the conference and selecting papers for this special issue. We also thank Professors Kaoru Hirota and Hung T. Nguyen for their ongoing support for InTech since its inception in 2000.

: pp. 566-572
Application of Kernel Trick to Fuzzy c-Means with Regularization by K-L Information
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Hidetomo Ichihashi, and Katsuhiro Honda
: pp. 573-581
A Fast Dynamic Full-Text Search Method Using Efficient Block Management Structure
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Masao Fuketa, El-Sayed Atlam, Elmarhomy Ghada, and Jun-ichi Aoe
: pp. 582-590
Accelerated Genetic Programming for Intelligent Fuzzy Robots
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Yasuyuki Murai, Koki Matsumura, Hisayuki Tatsumi, Hiroyuki Tsuji, and Shinji Tokumasu
: pp. 591-598
Weighted Similarity Classifier Using Differential Evolution and Genetic Algorithm in Weight Optimization
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Pasi Luukka, and Jouni Sampo
: pp. 599-605
Comparison of Linguistic and Regular Hard C-Means in Postoperative Patient Data
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Sansanee Auephanwiriyakul, and Nipon Theera-Umpon
: pp. 606-612
Leeway Prediction of Oceanic Disastrous Target via Support Vector Regression
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Nipon Theera-Umpon, and Udomsak Boonprasert
: pp. 613-620
Single-Channel Noise Reduction for Multiple Background Noises Using Perceptual Wavelet Packet Transform and Fuzzy Logic
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Montri Phothisonothai, Pinit Kumhom, and Kosin Chamnongthai
: pp. 621-626
Studies on Effects of Initialization on Structure Formationand Generalization of Structural Learning with Forgetting
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Hiroshi Shiratsuchi, Hiromu Gotanda, Katsuhiro Inoue, and Kousuke Kumamaru
: pp. 627-632
Separability Conditions for Multilayer Nets Having Solutions and Convergent Superiority of Bipolar Nets
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Hiroshi Shiratsuchi, Hiromu Gotanda, Katsuhiro Inoue, and Kousuke Kumamaru
: pp. 633-638
Hierarchical Concept Structures Based Data Retrieval/Mining by Fuzzy Document Ordering System
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Tadashi Ohashi, Hajime Nobuhara, and Kaoru Hirota
: pp. 639-648
Color Instance-Based Reasoning and its Application to Dynamic Image Restoration Under Low Luminance Conditions
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Yutaka Hatakeyama, Kazuhiko Kawamoto, Hajime Nobuhara, Shin-ichi Yoshida, and Kaoru Hirota

No.5

(Sep)

Computational Cybernetics

Computational Cybernetics

: p. 453
Computational Cybernetics
Wilfried Elmenreich, and Imre J. Rudas

This issue contains selected papers from the International IEEE Conference on Computational Cybernetics that took place in August 2003 in Hungary at the site of lake Balaton. Computational Cybernetics is the synergetic integration of Cybernetics and Computational Intelligence techniques. Cybernetics was defined by Wiener as “the science of control and communication, in the animal and the machine”. The word “cybernetics” itself stems from the Greek “kybernetes” that means pilot or governor. Thus, the science of computational Cybernetics is especially concerned with the comparative study of automatic control systems. Furthermore, Computational Cybernetics covers not only mechanical, but biological (living), social and economical systems and for this uses computational intelligence based results of communication theory, signal processing, information technology, control theory, the theory of adaptive systems, the theory of complex systems (game theory, operational research), and computer science. We have selected 14 papers from the conference covering the fields of system design and modeling, neural networks, and fuzzy control, which resemble the great variety of computational cybernetics. While it is sometimes difficult to integrate over these differing fields, we expect the evolution of future intelligent systems at the service of mankind by the synergetic integration of these different areas. It is our hope that the papers in this issue will inspire and help our readers in the development of advanced intelligent systems.

: pp. 454-459
Concept-Based Interactive Brainstorming in Engineering Design
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Gideon Avigad, Amiram Moshaiov, and Neima Brauner
: pp. 460-468
Translated Multiplicative Neuron: An Extended Multiplicative Neuron that can Translate Decision Surfaces
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Eduardo Masato Iyoda, Hajime Nobuhara, and Kaoru Hirota
: pp. 469-476
Motion-Based Template Matching for Obstacle Detection
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Kazuhiko Kawamoto, Naoya Ohnishi, Atsushi Imiya, Reinhard Klette, and Kaoru Hirota
: pp. 477-481
Learning from Examples and Comparing Models of Human Motion
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Marek Kulbacki, Bartosz Jablonski, Ryszard Klempous, and Jakub Segen
: pp. 482-487
Microstatistic Multi-User Detection Receiver
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Dusan Kocur, Jana Cízová, and Stanislav Marchevský
: pp. 488-494
Anytime System Scheduler for Insufficient Resource Availability
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Annamária R. Várkonyi-Kóczy, and Gábor Samu
: pp. 495-498
Energy-Conservative Algorithm for the Numerical Solution of Initial-Value Hamiltonian System Problems
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Edit Miletics
: pp. 499-506
Development of Fuzzy Controllers with Dynamics Regarding Stability Conditions and Sensitivity Analysis
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Radu-Emil Precup, Stefan Preitl, and Péter Korondi
: pp. 507-513
Evolutionary Design of Combinational Logic Circuits
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Cecília Reis, J. A. Tenreiro Machado, and J. Boaventura Cunha
: pp. 514-522
A Combined Solution of the Inverse Kinematic Task in the Vicinity of the Singularities
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Imre J. Rudas, József K. Tar, János F. Bitó, Ágnes Szeghegyi, and Krzysztof R. Kozlowski
: pp. 523-529
A Test Model for Hardware and Software Systems
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József Sziray
: pp. 530-534
New Method of Program Selection in Digital TV Receivers and its Evaluation by Users
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Petr Weissar, Jiri Pinker, Miloslava Hrichova, and Jaroslav Firt
: pp. 535-543
Kalman-Filter Based Control and Performance Monitoring Systems
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Attila L. Bencsik
: pp. 544-552
Possibilities for Application of Associative Objects with Built-in Intelligence in Engineering Modeling
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László Horváth, and Imre J. Rudas
: pp. 553-561
Sliding Mode Robot Control with Friction and Payload Estimation
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Lörinc Márton*, and Béla Lantos**

No.4

(Jul)

Special Issue on Papers Selected in ISIS & SCIS 2003

Special Issue on Papers Selected in ISIS & SCIS 2003

: p. 349
Papers Selected in ISIS & SCIS 2003
Yong-Soo Kim, and Kwee-Bo Sim

This special issue of journal covers a broad field ranging from intelligent systems to robotics. These papers were selected among the papers that were presented at the Joint 4th International Symposium on Advanced Intelligent Systems and 2nd International Conference on Soft Computing and Intelligent Systems which was held in Jeju, Korea on September 25-28, 2003. In the above symposium, there was a wide spectrum of intelligent systems and related topics, including sessions: intelligent systems, intelligent control, fuzzy sets, fuzzy systems, neural networks, robotics, genetic algorithms, image processing, soft computing, artificial life, etc. Many interesting results were presented at the symposium. Among these various papers, this special issue offers a selection of sixteen papers that contribute to advances of intelligent systems in various aspects. The topics that the selected papers deal with are fuzzy controller for the mobile robot control, neural networks and their application to image processing, intelligent control for a robot, intelligent system for probe detection, fuzzy image processing, genetic algorithms, fuzzy clustering for incomplete categorical data, predictive fuzzy controller for an electric four-wheeled vehicle. As guest editors of this special issue, we would like to express our thanks to authors for their contribution, the anonymous referees for their review, and Prof. Kaoru Hirota for his giving the opportunity to publish this special issue.

: pp. 350-355
Intelligent Soft Driving System for a Four-Wheeled Electric Vehicle Eluding Dynamic Obstacles
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Masaki Inoue, and Seiji Yasunobu
: pp. 356-361
Co-Evolution of Fuzzy Controller for the Mobile Robot Control
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Kwang-Sub Byun, Chang-Hyun Park, and Kwee-Bo Sim
: pp. 362-368
Fuzzy Descriptor System Modeling and Control of Lagrange Dynamics with Regional Pole-Placement Constraint
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Jin-Shig Kang
: pp. 369-372
Design and Analysis of Probe Detection Systems for TCP Networks
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Se-Yul Lee, and Yong-Soo Kim
: pp. 373-378
A Fuzzification of Morphological Wavelets Based on Fuzzy Relational Calculus and its Application to Image Compression/Reconstruction
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Hajime Nobuhara, and Kaoru Hirota
: pp. 379-384
Fuzzy Aggregation Method Using Fisherface and Wavelet Decomposition for Face Recognition
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Keun-Chang Kwak, Witold Pedrycz, Hyoun-Joo Go, and Myung-Geun Chun
: pp. 385-389
Recognition of Car License Plates Using Morphological Information and SOM Algorithm
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Kwang-Baek Kim, and Dae-Su Kim
: pp. 390-396
Intelligent Logo Watermarking Based on Independent Component Analysis
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Thai Duy Hien, Zensho Nakao, and Yen-Wei Chen
: pp. 397-402
Simultaneous Application of Fuzzy Clustering and Quantification with Incomplete Categorical Data
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Katsuhiro Honda, Yoshihito Nakamura, and Hidetomo Ichihashi
: pp. 403-409
Safe Mobility System Cooperating with Human in Collaboration with Cyber City
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Toru Yamaguchi, Jun Kawakatsu, Jianping Jing, Ryuji Kurosaki, and Fumio Harashima
: pp. 410-414
Expert-Knowledge Gating Mechanism in the Hierarchical Modular System
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Jeong-Yon Shim
: pp. 415-420
A Study on Supplier Evaluation and Selection Method Based on Dependence
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Jong-Moon Ju, and Seung-Gook Hwang
: pp. 421-425
A Proposal of GA Using Symbiotic Evolutionary Viruses and its Virus Evaluation Techniques
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Yoshiaki Sakakura, Noriyuki Taniguchi, Yukinobu Hoshino, and Katsuari Kamei
: pp. 426-430
Development of an Automatic Tunneling Algorithm Based on Fuzzy Logic for the Microtunneling System
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Taedong Park, Janghyun Nam, Jeong-Su Han, Jun-Hyeong Do, and Zeungnam Bien
: pp. 431-436
Fault Detection of Induction Motors Using Fourier and Wavelet Analysis
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Hyeon Bae, Youn-Tae Kim, Sungshin Kim, Sang-Hyuk Lee, and Bo-Hyeun Wang
: pp. 437-441
Reliability Optimization Problems Using Adaptive Hybrid Genetic Algorithms
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Minoru Mukuda, YoungSu Yun, and Mitsuo Gen
: pp. 442-450
A Fuzzy Approach for Modelling the Effects of Noise Pollution on Human Performance
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Zaheeruddin, and V. K. Jain

No.3

(May)

Perspectives of Computational Intelligence in Robotics and Automation

Perspectives of Computational Intelligence in Robotics and Automation

: pp. 235-236
Perspectives of Computational Intelligence in Robotics and Automation
Max Q-H Meng, and Hong Zhang

As people attempt to build biomimetic robots and realize automation processes through artificial intelligence, computational intelligence plays a very important role in robotics and automation. This special issue contains several important papers that address various aspects of computational intelligence in robotics and automation. While acknowledging its limited coverage, this special issue offers a range of interesting contributions such as intelligent trajectory planning for flying and land mobile robots, fuzzy decision making, control of rigid and teleoperated robots, modeling of human sensations, and intelligent sensor fusion techniques. Let us scan through these contributions of this special issue. The first paper, “Planar Spline Trajectory Following for an Autonomous Helicopter,” by Harbick et al., proposes a technique for planar trajectory following for an autonomous aerial robot. A trajectory is modeled as a planar spline. A behavior-based control system stabilizes the robot and enforces trajectory following of an autonomous helicopter with a reasonable trajectory tracking error on the order of the size of the helicopter (1.8m). In the second paper, “A Biologically Inspired Approach to Collision-Free Path Planning and Tracking Control of a Mobile Robot,” by Yang et al., a novel biologically inspired neural network approach is proposed for dynamic collision-free path planning and stable tracking control of a nonholonomic mobile robot in a non-stationary environment, based on shunting equations derived from Hodgkin and Huxley’s biological membrane equation. The third paper, “Composite Fuzzy Measure and Its Application to Decision Making,” by Kaino and Kaoru, builds a composite fuzzy measure from fuzzy measures defined on fuzzy measurable spaces using composite fuzzy weights by the authors, with a successful application to an automobile factory capital investment decision making problem. In “Intelligent Control of a Miniature Climbing Robot,” by Xiao et al., a fuzzy logic based intelligent optimal control system for a miniature climbing robot to achieve precision motion control, minimized power consumption, and versatile behaviors is presented with validation via experimental studies. The fifth paper, “Incorporating Motivation in a Hybrid Robot Architecture,” by Stoytchev and Arkin, describes a hybrid mobile robot architecture capable of deliberative planning, reactive control, and motivational drives, which addresses three main challenges for robots living in human-inhabited environments: operating in dynamic and unpredictable environment, dealing with high-level human commands, and engaging human users. Experimental results for a fax delivery mission in a normal office environment are included. In the next paper, “Intelligent Scaling Control for Internet-based Teleoperation,” by Liu et al., an adaptive scaling control scheme, with a neural network based time-delay prediction algorithm trained using the maximum entropy principle, is proposed with successful experimental studies on an Internet mobile robot platform. The next paper, “Feature Extraction of Robot Sensor Data Using Factor Analysis for Behavior Learning,” by Fung and Liu, discusses important knowledge extraction of sensor data for robot behavior learning using a new approach based on the inter-correlation of sensor data via factor analysis and construction of logical perceptual space by hypothetical latent factors. Experimental results are included to demonstrate the process of logical perceptual space extraction from ultrasonic range data for robot behavior learning. “Trajectory Planning of Mobile Robots Using DNA Computing,” by Kiguchi et al., presents an optimal trajectory planning method for mobile robots using Watson-Crick pairing to find the shortest trajectory in the robot working area with the DNA sequences representing the locations of the obstacles removed during the process. The proposed algorithm is especially suitable for computing on a DNA molecular computer. In the ninth paper, “Computational Intelligence for Modeling Human Sensations in Virtual Environments,” by Lee and Xu, cascade neural networks with node-decoupled extended Kalman filter training for modeling human sensations in virtual environments are proposed, with a stochastic similarity measure based on hidden Markov models to calculate the relative similarity between model-generated sensations and actual human sensations. A new input selection technique, based on independent component analysis capable of reducing the data size and selecting the stimulus information, is developed and reported. The next paper, “Intelligent Sensor Fusion in Robotic Prosthetic Eye System,” by Gu et al., is concerned with the design, sensing and control of a robotic prosthetic eye that moves horizontally in synchronization with the movement of the natural eye. It discusses issues on sensor failure detection and recovery and sensor data fusion techniques using statistical methods and artificial neural network based methods. Simulation and experimental results are included to demonstrate the effectiveness of the results. The final contribution in our collection is a paper by Sun et al., entitled “A Position Control of Direct-Drive Robot Manipulators with PMAC Motors Using Enhanced Fuzzy PD Control.” It presents a simple and easy-to-implement position control scheme for direct-drive robot manipulators based on enhanced fuzzy PD control, incorporating two nonlinear tracking differentiators into a conventional PD controller. Experiments on a single-link manipulator directly driven by a permanent magnet AC (PMAC) motor demonstrate the validity of the proposed approach. The Guest Editors would like to thank the contributors and reviewers of this special issue for their time and effort in making this special issue possible. They would also like to express their sincere appreciation to the JACIII editorial board, especially Profs. Kaoru and Fukuda, Editors-in-Chief and Kenta Uchino, Managing Editor, for the opportunity and help they provided for us to put together this special issue.

: pp. 237-242
Planar Spline Trajectory Following for an Autonomous Helicopter
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Kale Harbick, James F. Montgomery, and Gaurav S. Sukhatme
: pp. 243-251
A Biological Inspired Approach to Collision-Free Path Planning and Tracking Control of a Mobile Robot
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Simon X. Yang, Max Q.-H. Meng, Gavin X. Yuan, and Peter X. Liu
: pp. 252-259
Composite Fuzzy Measure and its Application to Decision-Making
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Toshihiro Kaino, and Kaoru Hirota
: pp. 260-268
Intelligent Control of a Miniature Climbing Robot
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Jizhong Xiao, Jun Xiao, and Ning Xi
: pp. 269-274
Incorporating Motivation in a Hybrid Robot Architecture
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Alexander Stoytchev, and Ronald C. Arkin
: pp. 275-283
Intelligent Scaling Control for Internet-Based Teleoperation
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Peter Xiaoping Liu, Max Q-H Meng, and Jason J. Gu
: pp. 284-294
Feature Extraction of Robot Sensor Data Using Factor Analysis for Behavior Learning
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Wai-keung Fung, and Yun-hui Liu
: pp. 295-301
Trajectory Planning of Mobile Robots Using DNA Computing
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Kazuo Kiguchi, Keigo Watanabe, and Toshio Fukuda
: pp. 302-312
Computational Intelligence for Modeling Human Sensations in Virtual Environments
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Ka Keung Lee, and Yangsheng Xu
: pp. 313-323
Intelligent Sensor Fusion in Robotic Prosthetic Eye System
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Jason J. Gu, Max Meng, Albert Cook, and Peter Xiaoping Liu
: pp. 324-331
Position Control of Direct-Drive Robot Manipulators with PMAC Motors Using Enhanced Fuzzy PD Control
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Dong Sun, Y. X. Su, and James K. Mills
: pp. 332-335
Novel Approach to Decision-Tree Construction
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Wei Jin-Mao, Wang Shu-Qin, and Wang Ming-Yang
: pp. 336-345
Solving Truck Delivery Problems Using Integrated Evaluation Criteria Based on Neighborhood Degree and Evolutionary Algorithm
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Fangyan Dong, Kewei Chen, Eduardo Masato Iyoda, Hajime Nobuhara, and Kaoru Hirota

No.2

(Mar)

Special Issue on Pattern Recognition

Special Issue on Pattern Recognition

: p. 83
Pattern Recognition
Osamu Hasegawa

We are pleased to publish this special JACIII issue on pattern recognition featuring 3 invited and 14 regular papers. The first and second concern support vector machines and Bayesian networks by authors who are field experts, and should serve as an introduction to beginners and a resource for researchers. In the third paper, the authors propose an artificial neural network for pattern recognition using “living” neural cells. This paper was invited because the research it deals with is considered an example of the interfield research expected to develop in the 21st century. The remaining 14 regular papers are reviewed and selected from 19 submitted papers. In reviewing and selecting the 14 regular papers, covering a broad field range from basic theory to applied systems, we focused on the originality of each paper and the viewpoints of the authors toward problems and experimental results. This wide-ranging selection should prove both innovative and enlightening to interested readers. We thank Professors Kaoru Hirota and Toshio Fukuda, editors-in-chief of this journal, for their support of this special issue. We also thank the staff of Fuji Technology Press for its invaluable assistance.

: pp. 84-92
Support Vector Machine and Generalization
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Takio Kurita
: pp. 93-99
Bayesian Network: Probabilistic Reasoning, Statistical Learning, and Applications
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Yoichi Motomura
: pp. 100-107
Operation of Spatiotemporal Patterns Stored in Living Neuronal Networks Cultured on a Microelectrode Array
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Suguru N. Kudoh, and Takahisa Taguchi
: pp. 108-114
Rapid Discriminative Learning
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Jun Rokui
: pp. 115-120
Robust Fuzzy Clustering Based on Similarity between Data
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Kohei Inoue, and Kiichi Urahama
: pp. 121-129
A Growing Neural Network for Online Unsupervised Learning
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Shen Furao, and Osamu Hasegawa
: pp. 130-139
A View-Invariant Face Detection Method Based on Local PCA Cells
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Kazuhiro Hotta
: pp. 140-149
Pointing Device Based on Estimation of Trajectory and Shape of a Human Hand in a Monocular Image Sequence
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Satoru Odo, and Kiyoshi Hoshino
: pp. 150-155
Human Limb Extraction Based on Motion Estimation Using Optical Flow and Image Registration
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Toru Tamaki
: pp. 156-167
Tool Operation Recognition Based on Robust Optical Flow and HMM from Short-Time Sequential Image Data
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Hidetomo Sakaino, Yutaka Yanagisawa, and Tetsuji Satoh
: pp. 168-180
Extraction of Motion Characteristics Corresponding to Sensitivity Information Using Dance Movement
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Shihoko Kamisato, Satoru Odo, Yoshino Ishikawa, and Kiyoshi Hoshino
: pp. 181-189
Automatic Video Recording of Lecture’s Audience with Activity Analysis and Equalization of Scale for Students Observation
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Satoshi Nishiguchi, Yoshinari Kameda, Koh Kakusho, and Michihiko Minoh
: pp. 190-199
Joint Audio-Visual Tracking Based on Dynamically Weighted Linear Combination of Probability State Density
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Masaru Tsuchida, Takahito Kawanishi, Hiroshi Murase, and Shigeru Takagi
: pp. 200-207
Printed Japanese Character Recognition Using Multiple Commercial OCRs
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Hidetoshi Miyao, Yasuaki Nakano, Atsuhiko Tani, Hirosato Tabaru, and Toshihiro Hananoi
: pp. 208-215
Stave Extraction for Printed Music Scores Using DP Matching
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Hidetoshi Miyao, and Masayuki Okamoto
: pp. 216-222
Classification of Remotely Sensed Images Using Independent Component Analysis and Spatial Consistency
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Xiang-Yan Zeng, Yen-Wei Chen, and Zensho Nakao
: pp. 223-230
Behavior Learning and Animation Synthesis of Falling Flat Objects
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Kohta Aoki, Osamu Hasegawa, and Hiroshi Nagahashi

No.1

(Jan)

Selected Papers from Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM'03)

Selected Papers from Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM'03)

: p. 1
Selected Papers from Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM’03)
Elmer P. Dadios

I am happy and honored to publish a special issue on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) in the JACIII. The demand for the application of HNICEM is rapidly growing. We have received many papers focused in the fields of Manufacturing, Academe, and Business during the first international conference on HNICEM. This special issue includes 11 papers carefully and evenly selected from these fields. I strongly believed that the topics in this issue encompass technologies that will play a significant role in the development of HNICEM. I am very grateful to Prof. Toshio Fukuda and Prof. Kaoru Hirota, the Chief editors of JACIII for inviting me as Guest editor in this Journal. I would like to express my warmest thanks and gratitude to the distinguished authors that have contributed to this special issue, for their outstanding contributions and cooperation. I am hoping that the JACIII will continually provide papers regarding progress of new technologies particularly in the fields of HNICEM.

: pp. 2-6
A New Way of Discovery of Belief, Desire and Intention in the BDI Agent-Based Software Modeling
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Chang-Hyun Jo
: pp. 7-13
Integration of Distributed Robotic Systems
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Fakhri Karray, Rogelio Soto, Federico Guedea, and Insop Song
: pp. 14-22
A Searching and Tracking Framework for Multi-Robot Observation of Multiple Moving Targets
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Zheng Liu, Marcelo H. Ang Jr., and Winston Khoon Guan Seah
: pp. 23-28
Possibilistic Uncertainty Propagation and Compromise Programming in the Life Cycle Analysis of Alternative Motor Vehicle Fuels
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Raymond R. Tan, Alvin B. Culaba, and Michael R. I. Purvis
: pp. 29-38
Dynamic Color Object Recognition Using Fuzzy Logic
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Napoleon H. Reyes and Elmer P. Dadios
: pp. 39-44
An Optical Coordinate Measuring Machine for Nanoscale Dimensional Metrology
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Eric Kirkland, Thomas R. Kurfess, and Steven Y. Liang
: pp. 45-55
Humanoid Robot HanSaRam: Recent Progress and Developments
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Jong-Hwan Kim, Dong-Han Kim, Yong-Jae Kim, Kui-Hong Park, Jae-Ho Park, Choon-Kyoung Moon, Jee-Hwan Ryu, Kiam Tian Seow, and Kyoung-Chul Koh
: pp. 56-64
Generalized Associative Memory Models: Their Memory Capacities and Potential Application
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Teddy N. Yap, Jr. and Arnulfo P. Azcarraga
: pp. 65-71
Hybrid Fuzzy Logic Strategy for Soccer Robot Game
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Elmer A. Maravillas, Napoleon H. Reyes, and Elmer P. Dadios
: pp. 72-80
Image Compression and Reconstruction based on Fuzzy Relation and Soft Computing Technology
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Kaoru Hirota, Hajime Nobuhara, Kazuhiko Kawamoto, and Shin’ichi Yoshida

Vol.7 (2003)

No.3

(Oct)

Intelligent Technologies, Fuzzy Systems and Applications

Intelligent Technologies, Fuzzy Systems and Applications

: p. 251
Intelligent Technologies, Fuzzy Systems and Applications
Nguyen Hoang Phuong

In this issue, we are featuring fifteen papers devoted to intelligent technologies, fuzzy systems and their applications as a special issue of the journal. The papers are selected from papers accepted and presented at the joint Third International Conference on Intelligent Technologies and Third Vietnam-Japan Symposium on Fuzzy Systems and Applications (InTech/VJFuzzy’2002) that was held in Hanoi, Vietnam on December 3-5, 2002. In InTech/VJFuzzy’2002, there was a wide spectrum of research topics on artificial intelligence, fuzzy systems, soft computing, and related fields such as”fuzzy logic”, “fuzzy set theory”, “fuzzy systems”, “AI techniques”, “Bayesian networks”, “genetic algorithms”, “neural networks”, “knowledge discovery and data mining”, “speech recognition”, “soft computing in medicine”, among others. More than 60 papers were accepted and presented by authors from many countries, including Australia, China, India, Korea, Germany, France, Thailand, Taiwan, Japan, Vietnam, and U.S.A. Fifteen papers that received outstanding recommendations from its reviews were selected in this special issue. The topics addressed by these selected papers include fuzzy rule systems, fuzzy inference methods, fuzzy and rough models, problem solving with equivalent transformation, genetic algorithms, reinforcement learning, non–monotonic reasoning, support vector machines, Hedge algebra, intelligent control, natural language understanding, self–organizing map learning, soft computing and data mining in medicine. As editors of this special issue, we would like to express our sincere gratitude to paper’s authors in this issue. Our special thanks go to the anonymous referees for their excellent job, Ms. Kumiko Sato for her help in coordinating the publication of the issue, the Editorial Board of JACIII, especially Professor Kaoru Hirota for his great support and encouragement. Finally, we wish to thank Professors Hung T. Nguyen, Michio Sugeno and Pratit Santiprabhob for their help and contribution to InTech/VJFuzzy’2002. Without their support, the InTech/VJFuzzy’2002 and this issue would not be possible.

: pp. 252-259
Some Preliminary Results on the Stableness of Extended F-rule Systems
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Nguyen Thanh Thuy, Phan Duong Hieu, and Takahiro Yamanoi
: pp. 260-267
How to Make Programs from Problem Descriptions in the Equivalent Transformation Paradigm
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Takahiko Ishikawa, Kiyoshi Akama, and Hiroshi Mabuchi
: pp. 268-275
Modeling and Analysis of Genetic Algorithms Based on the Viewpoint of Mixture Systems
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Jun-ichi Imai, Hiroyuki Shioya, and Masahito Kurihara
: pp. 276-282
Analysis of a Method Improving Reinforcement Learning Agents’ Policies
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Daisuke Kitakoshi, Hiroyuki Shioya, and Masahito Kurihara
: pp. 283-288
A Recursively Axiomatizable Subsystem of Levesque’s Logic of Only Knowing
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Ho Ngoc Duc
: pp. 289-293
Real Time Adaptive Color Segmentation for Mars Landing Site Identification
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Tuan A. Duong and Vu A. Duong
: pp. 294-305
The Fuzzy Description Logic ALCFH with Hedge Algebras as Concept Modifiers
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Steffen Hölldobler, Hans-Peter Störr, and Tran Dinh Khang
: pp. 306-314
Integration of Syntactic Analysis and Semantic Interpretation Based on Equivalent Transformation
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Hiroshi Mabuchi, Kiyoshi Akama, Takahiko Ishikawa, and Hidekatsu Koike
: pp. 315-321
Reordering Adaptive Directed Acyclic Graphs for Multiclass Support Vector Machines
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Thimaporn Phetkaew, Wanchai Rivepiboon, and Boonserm Kijsirikul
: pp. 322-329
Hybrid Probabilistic Models of Fuzzy and Rough Events
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Rolly Intan, Masao Mukaidono, and Hung T. Nguyen
: pp. 330-338
Intelligent Control of Nonlinear Dynamic Systems Using Immune Fuzzy Fusion
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Dong Hwa Kim
: pp. 339-347
Fuzzy Modeling for Modifying Standard Prescriptions of Oriental Traditional Medicine
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Nguyen Hoang Phuong, Pratit Santiprabhob, and Kaoru Hirota
: pp. 348-354
Temporal Abstraction for Long-Term Test Changes in the Hepatitis Domain
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Saori Kawasaki, Trong Dung Nguyen, and Tu Bao Ho
: pp. 355-361
A Novel Parallel Model for Self-Organizing Map and its Efficient Implementation on a Data-Driven Multiprocessor
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Ruck Thawonmas, Makoto Iwata, and Satoshi Fukunaga
: pp. 362-369
Fuzzy Inference Methods Employing T-norm with Threshold and Their Implementation
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Bui Cong Cuong, Nguyen Hoang Phuong, Ho Khanh Le, Bui Truong Son, and Koichi Yamada
: pp. 370-376
Phase Transitions in Fuzzy Clustering Based on Fuzzy Entropy
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Makoto Yasuda, Takeshi Furuhashi, and Shigeru Okuma
: pp. 377-385
New Error Diffusion Using Fuzzy Threshold Control
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Noriaki Suetake, and Rumiko Hashiba

No.2

(Jun)

Selected Papers from SCIS & ISIS 2002

Selected Papers from SCIS & ISIS 2002

: p. 71
Selected Papers from SCIS & ISIS 2002
Sadaaki Miyamoto, and Seiji Yasunobu

Much work has been done recently in soft computing, reflecting the growing, widespread interest in the emerging theory and technological development in this field. Soft computing has also been the subject of a number of new scientific conferences and symposiums, including the Joint 1st International Conference on Soft Computing and Intelligent Systems (SCIS) and 3rd International Symposium on Advanced Intelligent Systems (ISIS) at the National Institute of Advanced Industrial Science and Technology at Tsukuba on October 21–25, 2002. The first and second ISIS were held in Korea, and the joint conference has become internationally recognized. The conference at Tsukuba featured over 200 papers and discussions and information exchange by over 250 participants interested in state–of–the–art soft computing. The 20 papers in this special issue were selected from 209 of these conference presentations. In the selection process, guest editors first requested recommendation of papers to session chairs and organizers. From the resulting list, we asked if they could submit their papers. Submitted papers were reviewed as usual for this journal based on the JACIII standard, resulting in the acceptance of these 20. Most papers have been rewritten and updated, and we are proud of their high quality–a reflection of the SCIS & ISIS conference review process. Papers come from different areas: two focus on theory, including modal logic. Several study fuzzy control. Still others deal with different aspects of robotics. They also cover data analysis, image analysis, knowledge analysis, and language studies involving soft computing. This issue thus provides a concise summary of state–of–the–art soft computing methodologies. We thank the referees for their untiring efforts to complete reviews within the limited time given. We also thank Professor Kaoru Hirota and Professor Toshio Fukuda, editors–in–chief of this journal, for their kind invitation to this special issue. Professor Hirota, who is also the International Advisory Board Chair, and Dr. Takanori Shibata, the General Chair, are largely responsible for the great success of the SCIS & ISIS 2003 conference, where Guest Editor Miyamoto served the Program Chair and Yasunobu served the Special Sessions Chair. The results of their work are thus reflected in this issue. In closing, we also thank the staff at Fuji Technology Press for its on going assistance.

: pp. 72-78
Modifier Logics Based on Graded Modalities
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Jorma K. Mattila
: pp. 79-85
Cardinal-Probabilistic Interaction Indices and their Applications: A Survey
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Katsushige Fujimoto
: pp. 86-91
Adaptive Fuzzy Control for a SISO Nonlinear System
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Hugang Han, and Shuta Murakami
: pp. 92-100
Adaptive Fuzzy Control of One Linear Actuator Hopping Robot
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Son Kuswadi, Mitsuji Sampei, and Shigeki Nakaura
: pp. 101-107
Response Control of Variable Stiffness Structure Using Electromagnetic Clutch
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Toshihiro Irie, Kiyoshi Shingu, Keita Kitamura, and Yoshihiro Takagi
: pp. 108-114
A Study on a Foraging Behavior of Interacting Simple Robots
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Ken Sugawara, Masaki Sano, and Toshinori Watanabe
: pp. 115-123
Obstacle Avoidance for Quadruped Robots Using a Neural Network
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Tomohiro Yamaguchi, Keigo Watanabe, Kiyotaka Izumi, and Kazuo Kiguchi
: pp. 124-129
Automatic Generation of Expressive Body Movement Based on Cohen-kestenberg Lifelike Motion Stereotypes
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Toru Nakata
: pp. 130-138
Psychological and Social Effects of Robot-assisted Activity in the Elderly Robot-assisted at Health Service Facilities
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Kazuyoshi Wada, Takanori Shibata, Tomoko Saito, and Kazuo Tanie
: pp. 139-146
A Powerful Neural Network Method with Digital-contract Hints for Pricing Complex Options
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Jun Lu, and Hiroshi Ohta
: pp. 147-152
Electricity Demand and Price Analysis in California Using Possibility Regression Model
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Osamu Hirano, Masayasu Kanke, and Kazuhiro Ozawa
: pp. 153-159
Fuzzy Microaggregation for Microdata Protection
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Josep Domingo-Ferrer, and Vicenç Torra
: pp. 160-168
Fuzzy Information Granules: a Compact, Transparent and Efficient Representation
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Giovanna Castellano, Anna Maria Fanelli, and Corrado Mencar
: pp. 169-177
An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations -Rough Clustering-
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Shoji Hirano and Shusaku Tsumoto
: pp. 178-188
Synergetic Stereo Matching Algorithm for Occlusion and Reversal Position
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Tohru Irie, Hiroshi Maeda, and Norikazu Ikoma
: pp. 189-199
Interactive 3-D Segmentation of the Frontal Lobe in 3.0T IR-FSPGR MR Images Using Fuzzy Rule-Based ACM
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Yuji Fujiki, Syoji Kobashi, Mieko Matsui, Noriko Inoue, Katsuya Kondo, Yutaka Hata, and Tohru Sawada
: pp. 200-206
A Sequential Method for Combining Random Utility Model and Fuzzy Inference Model
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Backjin Lee, Akimasa Fujiwara, Yoriyasu Sugie, and Moon Namgung
: pp. 207-214
Fuzzy Traffic Controller in Ramp Metering of Urban Expressway
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Masashi Okushima, Yoshiharu Takihi, and Takamasa Akiyama
: pp. 215-222
A New Communication Method Using Natural Language as a Computer Communication Protocol
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Ichiro Kobayashi, Toru Sugimoto, Shino Iwashita, Michiaki Iwazume, Jun Ozawa, and Michio Sugeno
: pp. 223-228
Processing Technical Daily Reports in Offshore Petroleum Engineering – An Experience
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Kazuo Miura, Ivan Rizzo Guilherme, Celso Kazuyuki Morooka, and José Ricardo Pelaquim Mendes
: pp. 229-234
Properties of Interval Truth Values with Certainty Factor
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Masashi Emoto and Masao Mukaidono
: pp. 235-243
High-quality Multi-level Error Diffusion Method Employing Fuzzy Inference
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Noriaki Suetake and Masanori Togashi

No.1

(Feb)

Selected Papers FSS2002

Selected Papers FSS2002

: p. 1
Selected Papers FSS2002
Tetsuhisa Oda

Research in fuzzy system theory and its application has progressed rapidly in Japan since the first Fuzzy System Symposium (FSS) in 1985. This national meeting has been held annually for reading research papers by fuzzy system theory researchers. The Japan Society for fuzzy system theory and Systems (SOFT), set up in 1989, was made the SOFT’s official annual meeting. The 18th FSS (FSS 2002), held at the Nagoya Institute of Technology, Nagoya, Japan, from August 28 to 30, attracted over 320 participants and was the site of 197 lectures. At this FSS,Kaoru Hirota, President of the Society, declared, “It is necessary for researchers in fuzzy system theory in Japan to present results of their study in English for readers overseas. I am happy to announce that our society is to publish a journal of collected papers in English 3 times a year, in addition to the society journal in Japanese, entitled the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII).’ ” The initial result is this special February 2002 issue, which contains papers from preparatory papers read at FSS2002. Editing of this special issue was entrusted to the executive committee of FS2002, with the symposium chair acting as the guest editor. Other members of the editing committee are Hitoshi Yano, Nagoya City University; Moritoshi Sasaki, Aichi University of Education; Yahachiro Tsukamoto, Meijo University; Takeshi Furuhashi, Mie University; Yasuhisa Hasegawa, Nagoya University; Miho Ohsaki, Shizuoka University; Hiroto Mizunuma, Meijyo University; Tomohiro Yoshikawa, Mie University; and Tsuyoshi Nakamura, Nagoya Institute of Technology. We thank the committee members and referees for paper selection , and Kumiko Satoh of Fuji Technology Press Ltd. for clerical work associated with the preparation of the journal. Most papers have been rewritten by the authors for this publication. Two are written in English and 7 in Japanese and translated into English after selection. At least 2 referees read each paper to select the final 9. Subjects include fuzzy data base, learning, fuzzy clustering, application for marketing, industrial application, psychological application, and fuzzy logic. All research is original and represents the current level and trends in fuzzy system theory research in Japan. Unfortunately, circumstances forced us to select papers from among 4-page preparatory papers despite the possibilities in 2-page papers. If, however, the FSS special issues of JACIII every year, up-to-date papers prepared by Japanese researchers will be available for review by overseas readers with a possible significant contribution to the research of fuzzy system theory worldwide. This will be a good opportunity for Japanese researchers to make their results known overseas, making participation in FSS even more meaningful. In January 2003, the official name of SOFT was changed to the Japan Society for fuzzy system theory and Intelligent Informatics. We sincerely hope that JACIII will become a useful tool for presenting the latest fuzzy system theory research in Japan to the world, and, in turn, support indirectly the society’s development.

: pp. 2-5
Query Expansion Using Conceptual Fuzzy Sets for Search Engines
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Masanori Tajima, Takayuki Kawabata, Tomoe Tomiyama, and Tomohiro Takagi
: pp. 6-9
Effective Use of Learning Knowledge by FEERL
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Yukinobu Hoshino and Katsuari Kamei
: pp. 10-18
Acquisition of Knowledge for Gymnastic Bar Action by Active Learning Method
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Yoshitaka Sakurai, Nakaji Honda, and Junji Nishino
: pp. 19-24
Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines
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Sadaaki Miyamoto and Youichi Nakayama
: pp. 25-30
Fuzzy c-Means Clustering Using Kernel Functions in Support Vector Machines
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Sadaaki Miyamoto and Daisuke Suizu
: pp. 31-39
A Fuzzy Inference System for Identifying Tissue Elasticity Using Ultrasound
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Tadashi Kimura, Kouki Nagamune, Syoji Kobashi, Katsuya Kondo, Yutaka Hata, and Kazuhiko Taniguchi
: pp. 40-46
An Agent System Using Basic Emotions as Communication Method
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Yosuke Dendo and Katsuari Kamei
: pp. 47-52
Fuzzy Three-valued Switching Functions Using Choquet Integral
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Eiichiro Takahagi
: pp. 53-67
GMP Based Fuzzy Reasoning: An Application to Sonar Based Navigation
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Kudret Demirli, Burhan Türksen, and Mohammad Molhim

Vol.6 (2002)

No.3

(Oct)

Regular papers

Regular Papers

: pp. 100-108
Implementing Fuzzy Learning Algorithms in a 6 DOF Hydraulic Parallel Link Manipulator: Control with Actuators’ Forces Fuzzy Compensation
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Zakarya Zyada, Yasuhisa Hasegawa and Toshio Fukuda
: pp. 109-114
State Feedback Stabilization in Nonlinear Time-Delay Systems
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Tsuyoshi Hori and Kazuo Tanaka
: pp. 116-125
A Method for Recognizing and Separating Trademark Image Outer Frames
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Koji Abe, Haruhiko Kimura, Hideo Nagashima and Taki Kanda
: pp. 126-134
Sigma-Pi Cascade Extended Hybrid Neural Network
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Eduardo Masato Iyoda, Kaoru Hirota and Fernando J. Von Zuben
: pp. 135-144
A Study on Deriving a Method for Chromosome Similarities Suitable for the Search Space
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Yoshifumi Banno, Tomohiro Yoshikawa, Hiroharu Kawanaka, Tsuyoshi Shinogi and Shinji Tsuruoka

No.2

(Jun)

Regular papers

Regular Papers

: pp. 72-78
Modeling Photon Counting Experiments using Fuzzy Logic Controller
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László Nádai
: pp. 79-83
Contribution to Creation of Complex System Macrosituations
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Eva Ocelíková and Ladislav Madarász
: pp. 84-92
Online Neofuzzy Neuron Flux Observer for Induction Motor Drives
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Regis P. Landim, Francisco A. S. Neves, Selenio R. Silva, Walmir M. Caminhas** and Benjamim R. Menezes**
: pp. 93-98
Kinematic Robustness of Manipulating Systems
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Alexandra M.S.F. Galhano and J. A. Tenreiro Machado

No.1

(Jan)

Selected Papers VJFUZZY'2001

Selected Papers VJFUZZY'2001

: p. 1
Selected Papers VJFUZZY’2001
Nguyen Hoang Phuong*and Koichi Yamada**

This special issue of the journal is devoted to Fuzzy Systems and their Applications. Today’s fuzzy technology is like a mighty dragon: every year, every day, new applications appear that are more and more helpful in our peaceful life. One of the main objectives of the Second International Vietnam-Japan Symposium on Fuzzy Systems and Applications (VJFUZZY’2001) is to help this powerful technology to achieve even more. VJFUZZY’2001 was held in Hanoi, Vietnam on December 7-8, 2001. In VJFUZZY’2001, there was a wide spectrum of fuzzy research and applications, including sessions: “Fuzzy Mathematics”, “Fuzzy Image/Signal Processing and Pattern recognition”, “Fuzzy Optimization/Programming”, “Fuzzy Data Analysis and its applications”, “Towards Combining Fuzzy, probability and other techniques”, “Learning, Neural Networks and Applications”, “Fuzzy/Intelligent Control”, “Natural Language Processing” etc. Many very interesting results were presented at the symposium. Among these various and excellent papers, this special issue offers a selection of nine papers that contribute to advances of computational intelligence in various aspects. The topics that the selected papers deal with are fuzzy relation with threshold, nonlinear optimization with convex polyhedral objective function, reinforcement learning of fuzzy control rules, learning of probabilistic causal models, data querying in fuzzy relational database, case-based reasoning in medial diagnosis, analysis of human brain activities for fuzzy and crisp calculations, fuzzy robust control of a decentralized system and image processing using fuzzy mathematical morphology. As editors of this special issue, we would like to express our thanks to all of the contributors for their interesting results, the anonymous referees for their thankless job and the Editorial Board of JACI, especially Prof. Kaoru Hirora, for the enthusiastic hospitality with which JACI agreed to have this special issue. Finally, we want to thank Prof. Hung T Nguyen and Prof. Michio Sugeno – for their supports to VJFUZZY’2001. Without their help, the VJFUZZY’2001 and then this special issue would not be impossible.

: pp. 2-6
Fuzzy Relation with Thresholds and Applications
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Bui Cong Cuong, Nguyen Hoang Phuong, Phan Hoang Anh, and Koichi Yamada
: pp. 7-18
Effective Linear Calculational Method for Nonlinear Optimization with a Convex Polyhedral Objective Function and Linear Constraints
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Busaba Phruksaphanrat and Ario Ohsato
: pp. 19-24
Reinforcement Leaning of Fuzzy Control Rules with Context-Specitic Segmentation of Actions
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Hideki Yamagishi, Hiroshi Kawakami, Tadashi Horiuchi, and Osamu Katai
: pp. 25-32
Leaning Causal Models with Conditional Causal Probabilities from Data
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Koichi Yamada
: pp. 33-40
Approximate Data Querying in Fuzzy Relational Database
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Rolly Intan, and Masao Mukaidono
: pp. 41-50
Developing Case-based Reasoning System for Medical Consultation Using the Importance of Features
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Nguyen Hoang Phuong, Nguyen Ba Tu and Ario Ohsato
: pp. 51-55
Difference in Areas of the Brain for Fuzzy and Crisp Calculation
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Takahiro Yamanoi, Masaaki Saito, Michio Sugeno and EIie Sanchez
: pp. 56-61
Decentralized Robust Fuzzy Sliding Mode Control Design of Interconnected Uncertain System
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Thai Quang Vinh, and Kaoru Hirota
: pp. 62-69
Application of Fuzzy Mathematical Morphology with Adaptive Structuring Elements to Seal Defect Testing
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Takuo Kikuchi and Shuta Murakami

Vol.5 (2001)

No.6

(Nov)

Regular papers

Regular Papers

: pp. 307-314
Intelligent Hybrid System: A Reliability-Based Failure Management Application
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Jose Aguilar, Mariela Cerrad and Katiuska Morillo
: pp. 315-325
Fuzzy Difference Equations: The Initial Value Problem
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James J. Buckley, Thomas Feuring and Yoichi Hayashi
: pp. 326-332
Toward a Bioinspired Fusion of Color and Infrared Textural Image Information
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Javier Ruiz-del-Solar and Aureli Soria-Frisch
: pp. 333-337
Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control
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Takahiro Kobayashi and Tetsuji Tani
: pp. 338-345
Tracking Control of Variable Structure System Using Variable Boundary Layer
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Heejin Lee

No.5

(Sep)

Selected Papers INES2000

Selected Papers INES2000

: p. 247
Selected Papers INES2000
Imre J. Rudas*and Leon Zlajpah**

In engineering practice we often have to deal with complex systems, where the conventional approaches for understanding and predicting the behavior of the system can prove to be inadequate. Hence, the researchers try to put some intelligence into the system. The term intelligence in this context still more or less remains a mysterious phenomenon and can be characterized by different abilities of the system or machine, such as adaptation, decision-making, learning, recognition, diagnostics, autonomy, etc. Many of the new results related to this area are published in Journals and in International Conference Proceedings. One such conference is the “IEEE International Conference on Intelligent Engineering Systems”. The fourth conference in this series (INES 2000) took place in Portoroz, Slovenia, on September 17-19,2000. There were around eighty participants from eighteen countries around the world. We are glad that so many authors have contributed to ideas related to the issues at the conference. Many of the papers were about applications and design, and others on more theoretical aspects of intelligent systems. This variety made the selection of papers for this special issue very difficult. Eight papers have been selected in the end, which cover different aspects of intelligent engineering systems. It should be pointed out that the respective authors were also kind to revise and update the presented papers for this special issue. The first paper deals with the manipulation problem where the motion changes depending on the state of the system as it is the case in the finger gaiting applications. To solve it the semi-stratified control theory using smooth motion planning is used. The proposed concept combines the stratified motion planning with the unconstrained finger allocations. In the second paper a special branch of Soft Computing developed for the control of mechanical devices is described. It reduces the number of free parameters and computational complexity. For illustration of the efficiency of the proposed adaptive control, a simulation of polishing with a 3 DOF robot is given. The next paper discusses the force control of redundant robots in an unstructured environment. A special attention is given to the decoupling of the task space and null space motion. For that the minimal null space approach is used. The proposed impedance controller assures good task space performances and minimizes the disturbances caused by obstacles. The performance of the proposed controllers has been evaluated by the simulation and by experiments on a real robot. The forth paper presents some advanced modeling approaches and methods. As one of the key issues a manufacturing process model fully associative with form feature based part model has been introduced. The motivation has been that the low level integration of design and manufacturing of mechanical parts, as identified by the authors, is still a main drawback of efficient application of expensive modeling systems. The proposed method allows for creating part model simultaneously with their analysis of machineability. The next paper discusses the design of fractal-order discrete-time controllers. Some approaches to implement fractal derivatives and integrals are analyzed. As the application of the theory of fractional calculus is rather new, many aspects remain to be investigated. The sixth paper demonstrates how to map classical dictionaries and similar structured data to a hypertext structure that is more suitable for the modern media. To achieve the new shape automatically, the HiLog language is used. The automated mapping is illustrated by an example based on Oxford Dictionary of Modern English. In the seventh paper a humanoid robotics shoulder is compared to the human shoulder. First, the capabilities of the robotics shoulder are analyzed and next, using the optical measurement system the human shoulder movements have been measured and analyzed. The last paper discusses the bias-variance tests on multi-layer perception. The performance of Bayesian neural networks is compared with the performance of neural networks trained with a gradient method. Additionally, it is analyzed if it is possible to use a number of networks in committee trained with gradient descent to achieve the performance of a Bayesian network.

: pp. 248-256
Semi-stratified Motion Planning of Multi-agent Manipulation
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István Harmati, Ma Lantos and Shahram Payandeh
: pp. 257-262
Symplectic Geometry Based Simple Algebraic Possibilities for Developing Adaptive Control for Mechanical Systems
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József K. Tar, Imre J. Rudas, János F. Bitó and Seppo J. Torvinen
: pp. 263-268
Experiments with Force Control of Redundant Robots in Unstructured Environment Using Minimal Null-space Formulation
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Bojan Nemec and Leon Zlajpah Institute Jozef Stefan
: pp. 269-278
Virtual Technology Based Associative Integration of Modeling of Mechanical Parts
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Lász1ó Horváth and Imre J. Rudas
: pp. 279-285
Implementing Discrete-time Fractional-order Controllers
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J. A. Tenreiro Machado
: pp. 286-293
A HiLog Journey from a Classical Dictionary to Hypertext Structure (via the Grammar, of course)
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Mirko Cubrilo, Alen Lovrencic, and Mirko Malekovic
: pp. 294-299
Human Versus Robotic Shoulder Motion
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Nives Klopw and Jadran Lenarcic
: pp. 300-305
Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks
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Wimpie D. Nortje, Johann E. W. Holm, Gerhard P. Hancke, Imre. J. Rudas, and Laszlo. Horvath

No.4

(Jul)

Regular papers

Regular Papers

: pp. 193-200
Rule Extraction from Fuzzy Neural Networks FuNN: A Method and a Real-World Application
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Nikola Kasabov, Jaesoo Kim, Robert Kozma and Tico Cohen
: pp. 201-212
Multiagent Architecture Combined with a Multicontract Protocol for FMS Control
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Amar Khoukhi and Adlene Moualek
: pp. 213-219
Applying AHP to Preference Analysis by Dynamic Judgment-A Study of Adaptation and Identification of Odor
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Yoshihiro Ueda, Naotaka Kato, Haruhiko Kimura, Shinji Furukawa and Takashi Oyabu
: pp. 220-228
Evolving Neurofuzzy System by Hybrid Soft Computing Approaches for System Identification
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Shigeyasu Kawaji and Yuehui Chen
: pp. 229-238
Evolving Basis Function Networks for System Identification
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Yuehui Chen and Shigeyasu Kawaji
: pp. 239-245
A Fuzzy CDS-based Scheduling Algorithm for More Than Two Machine Centers
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Tzung-Pei Hong, Tzu-Ting Wang and Shyue-Liang Wang

No.3

(May)

Selected Papers from IFSA'99

Selected Papers from IFSA'99

: p. 127
Selected Papers from IFSA’99
Jonathan Lee* and Hsiao-Fan Wang**

The past few years we have witnessed a crystallization of soft computing as a means towards the conception and design of intelligent systems. Soft Computing is a synergetic integration of neural networks, fuzzy logic and evolutionary computation including genetic algorithms, chaotic systems, and belief networks. In this volume, we are featuting seven papers devoted to soft computing as a special issue. These papers are selected from papers submitted to the “The eighth International Fuzzy Systems Association World Congress (IFSA’99)”, held in Taipei, Taiwan, in August 1999. Each paper received outstanding recommendations from its reviewers. G-H Tzeng et al. integrate fuzzy numbers, fuzzy regression, and a fuzzy DEA approach as a performance evaluation model for forecasting the productive efficiency of a set of production units when some data are fuzzy numbers. A case of Taipei City Bus Company is adopted for illustration. Y. Shi et al. adopts a fuzzy programming approach to solve a MCMDM (multiple criteria and multiple decision makers) capital budget problem. A solution procedure is proposed to systematically identify a fuzzy optimal selection of possible projects. N. Nguyen et al. propose a new formalism (Chu spaces) to describe parallelism and information flow. Chu spaces provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations of membership functions or defuzzifications. M-C Su et al. propose a technique based on the SOM-based fuzzy systems for voltage security margin estimation. This technique was tested on 1604 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency. By defining the concept of approximate dependency and a similarity measure, S-L Wang et al. present a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. K.Yeh et al. use adaptive fuzzy sliding mode control for the structural control of bridges. Combing fuzzy control and sliding mode control can reduce the complexity of fuzzy rule bases and ensure the stability and robustness. This model is demonstrated by three types of bridges, with LRB, sliding isolators and no isolation device. Based on a novel fuzzy clustering algorithm, Y-H Kuo et al. propose an adaptive traffic prediction approach to generalize and unveil the hidden structure of traffic patterns with features of robustness, high accuracy and high adaptability. The periodical, Poisson and real video traffic patterns have been used to verify their approach and investigate its properties. We would like to express our sincere gratitude to everyone who has contributed to this special issue including the authors, the co-reviewers, the JACI Editors-in-Chief Toshio Fukuda and Kaoru Hirota.

: pp. 128-138
Application of Fuzzy Set Theory and DEA Model to Evaluating Production Efficiency for Taipei City Bus Company
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Gwo-Hshiung Tzeng, Cheng-Min Feng and Chao-Chung Kang
: pp. 139-148
Capital Budgeting with Multiple Criteria and Multiple Decision Makers: A Fuzzy Approach
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Yong Shi, Wikil Kwak, Heeseok Lee and Cheng-few Lee
: pp. 149-156
Chu Spaces: Towards New Foundations for Fuzzy Logic and Fuzzy Control, with Applications to Information Flow on the World Wide Web
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Nhu Nguyen, Hung T. Nguyen, Berlin Wu, Vladik Kreinovich
: pp. 157-162
Application of SOM-Based Fuzzy Systems in Voltage Security Margin Estimation
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Mu-Chun Su, Eugene Lai, Chee-Yuen Tew, Chih-Wen Liu and Chen-Sung Chang
: pp. 163-171
Answering Null Queries by Analogical Reasoning on Similarity-based Fuzzy Relational Databases
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Shyue-Liang Wang, Tzung-Pei Hong, and Wen-Yang Lin
: pp. 172-179
Application of Adaptive Fuzzy Sliding Mode Control for Bridges
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Ken Yeh and Wei-Ling Chiang
: pp. 180-188
An Adaptive Fuzzy Clustering Technique for Traffic Prediction of Packet-switched Networks
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Yau-Hwang Kuo, Mong-Fong Horng and Jung-Hsien Chiang

No.2

(Mar)

Regular papers

Regular Papers

: pp. 81-89
Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods
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Szilveszter Pletl and Bela Lantos
: pp. 90-98
Fast Iterative Solving Method of Various Types of Fuzzy Relational Equations and its Application to Image Reconstruction
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Hajime Nobuhara, Yasufumi Takama and Kaoru Hirota
: pp. 99-109
Fuzzification of Kolmogorov Theorem
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Angel López-Gótnez and Kaoru Hirota
: pp. 110-118
Approximate Reasoning for Processing Uncertainty
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Hamid Seridi and Herman Akdag
: pp. 119-124
Intelligent Consultant
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James J. Buckley, Thomas Feuring and Yoichi Hayashi

No.1

(Jan)

Selected Papers WISP'99

Selected Papers WISP'99

: p. 1
Selected Papers WISP’99
Annamária R. Várkonyi-Kóczy

Today’s complex industrial and engineering systems – especially with the appearance of large-scale embedded and/or real-time systems – confront researchers and engineers with completely new challenges. Measurement and signal processing systems are involved in almost all kinds of activities in that field where control problems, system identification problems, industrial technologies, etc., are to be solved, i.e., when signals, parameters, or attributes must be measured, monitored, approximated, or determined somehow. In a large number of cases, traditional information processing tools and equipment fail to handle these problems. Not only is the handling of previously unseen spatial and temporal complexity questionable but such problems have also to be addressed such as the interaction and communication of subsystems based on entirely different modeling and information expression methods, the handling of abrupt changes within the environment and/or the processing system, the possible temporal shortage of computational power and/or loss of some data due to the former. Signal processing should even in these cases provide outputs of acceptable quality to continue the operation of the complete system, producing data for qualitative evaluations and supporting decisions. It means the introduction of new ideas for specifying, designing, implementing, and operating sophisticated signal processing systems. Intelligent – artificial intelligence, soft computing, anytime, etc. – methods are serious candidates for handling many theoretical and practical problems, providing a better description, and, in many cases, are the best if not the only alternatives for emphasizing significant aspects of system behavior. These techniques, however, are relatively new methods and up until now, not widely used in the field of signal processing because some of the critical questions related to design and verification are not answered properly and because uncertainty is maintained quite differently than in classical metrology. After the initiation of the 1999 IEEE International Workshop on Intelligent Signal Processing, WISP’99, which was the first event to start linking scientific communities working in the fields of intelligent systems and signal processing and hoping that it will attract more and more scientists and engineers in these hot topics, this special issue continues this pioneering work by offering a selection of nine papers fitting into the profile of the journal from the numerous high quality ones presented at WISP’99. These excellent papers deal with different aspects of advanced computational intelligence in signal processing, including the application of neural networks, fuzzy techniques, genetic and anytime algorithms in modeling, signal processing, noise cancellation, identification, and pattern recognition, multisensorial information fusion and intelligent classification in image processing, exact and nonexact complexity reduction, and nonclassical and mixed data and uncertainty representation and handling. As an editor of this special issue, I would like to express my thanks to all of the contributors and my belief in that the excellent research results it contains provide the basis for further strengthening and spreading of advanced computational intelligence in signal processing opening wide possibilities for new theoretical and practical achievements.

: pp. 2-7
Anytime Evaluation of Regression-Type Algorithms
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Annamária R. Várkonyi-Kóczy, Tamás Kovácsházy, Orsolya Takács, and Csaba Benedescik
: pp. 8-14
Evolutionary Nonlinear Multimodel Partitioning Filters
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G. N. Befigiannis, E. N. Demiris and S. D. Likothanassis
: pp. 15-21
Speech Noise Cancellation Based on a Neuro-Fuzzy System: Further Improvements
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Anna Esposito, Eugene C. Ezin and Carlos A. Reyes-Garcia
: pp. 22-30
Inference Algorithm Independent SVD Fuzzy Rule Base Complexity Reduction
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Péter Baranyi, Yeung Yamb, Chi Tin Yang, Péter Várlakic, and Pél Michelberger
: pp. 31-36
An Innovative Way to Measure the Quality of a Neural Network Without the Use of a Test Set
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Giovanni Pilato, Filippo Sorbello and Giorgio Vassallo
: pp. 37-43
Rank-Based Multiple Classifier Decision Combination: A Theoretical Study
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Afsar Saranli and Mubeccel Demirekler
: pp. 44-50
Information Processing Based on Mixed – Classical and Fuzzy – Data Models
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Orsolya Takács and Annamária R. Várkonyi-Kóczy
: pp. 51-57
Optimizing and Learning Algorithm for Feed-forward Neural Networks
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Pilar Bachiller and Julia González
: pp. 58-70
A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model
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Jianqiang Yi, Naoyoshi Yubazaki and Kaoru Hirota
: pp. 71-77
Fuzzy Control Using Piecewise Linear Membership Functions Based on Knowledge of Tuning a PID Controller
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Kenichiro Hayashi, Akifumi Otsubo and Kazuhiko Shiranita

Vol.4 (2000)

No.6

(Nov)

Multimedia Information Compression Technologies

Multimedia Information Compression Technologies

: pp. 401-402
Multimedia Information Compression Technologies
T. D. Gedeon

Introduction We are drowning in data. What kinds of data? – Text. Images. Sound. Numeric. Genome data. Text: Every day vast amounts of textual data are generated. This ranges from private corporate data, personal information, public and private government documents and so on. Much of this data needs to be accessed by many users for many tasks. For example, a corporate call centre needs fast access to documents at a semi-concept level to answer user requests. Another example: large litigations can involve 2 million documents, 200,000 of which are relevant, much fewer significant, and a handful pivotal. Techniques are desperately needer to automate the first few steps of this winnowing. Images: There are video cameras everywhere, trying to protect our safety in car parks, public places, even some lifts. There are huge and ever growing still and video archives of all aspects of our modern world. Access and indexing this data is a huge research enterprise. Much indexing is done manually. Sound: Often in concert with video in multi-media recordings. But what did the Prime Minister say on the 1st of November about the Republic? Did he sound like he meant it? These are currently not easily answered queries except if carried out by an expert human investigator. These kind of queries will need to be commonplace to access sound data in humanly meaningful ways. Numeric: Our industries generate vast amounts of valuable numeric data. In the petroleum industry geologic knowledge must be integrated with data from wells: laboratory core analysis data and on-site well logs, with seismic data generated from controlled explosions and dispersed recording devices. Then there is GIS data collected from satellites and so on. In the service industry, the stock exchange generates large amounts of hard to analyse data vital to the wellbeing of Australian companies. Genome data: The human genome project is almost complete. Researchers are finding genes by a mix of laboratory work and computerised database searches (e.g. as reported in the Weekend Australian 30 October). This is just the first step, the next will be sequencing of a number of individuals, and of course there are currently over 100 whole genome sequencing projects on other species. Fast genome sequencing is just around the corner. We will soon be drowning in this kind of data also. Multimedia data: Includes all of audio, text, graphics, images, video, animation, music. More data! What Is The Real Problem? Manual extraction of information from any large corpus is time con-suming and expensive, requiring specialised experience in the material. Even worse, beyond a certain point it is incredibly boring, and hence error prone. Human intelligence is best suited to dealing with information, as distinct to data! A Solution The development of automated systems for information extraction, and for the synthesis of the extracted information into humanly useful information resources. To avoid drowning in the ever increasing flow of multi-modal electronic information available, automated tools are required to reduce the cognitive load on users. STEPS TOWARDS A SOLUTION The key step towards a solution is the notion of information compression, being the compression of data to yield an information rich(er) resource. This is distinct from data compression which is merely the efficient storage of data. Further, the information compression must work on multi-model complex data, exemplified by multimedia data. Some of the techniques for doing this kind of information compression exist in a scattered way in areas such as fuzzy systems, and image analysis. We have identified a nascent field, which we can coalesce in an intensive short workshop. The first Australia-Japan Joint Workshop on Applications of Soft and Intelligent Computing to Multimodal and Multimedia Information Compression Technologies was held at Murdoch University in Perth, Western Australia from the 29 March to 5 April 2000. This special issue contains selected papers from the workshop.

: pp. 403-407
A Methodology for Developing Adaptive Fuzzy Cognitive Maps for Decision Support
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M. Shamim Khan, Alex Chong, and Tom Gedeon
: pp. 408-411
Fuzzy Control of Back-Propagation Training
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Michael Negnevitsky and Martin J. Ringrose
: pp. 412-416
Indexing Visual Features Using a Hybrid Neural Network
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Jesse S. Jin, Henry C. Wang and Tom Gedeon
: pp. 417-420
Improving the Approximation Smoothness of Radial Basis Neural Networks
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Anthony Little and Leonid Reznik
: pp. 421-427
Implementation of Fuzzy Legal Expert System FLES
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Kaoru Hirota, MingQiang Xu, Yasufumi Takama and Hajime Yoshino
: pp. 428-436
Kansei Information Processing in Multimedia Applying Intelligent Soft Computing Techniques
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Takehisa Onisawa
: pp. 437-442
Robotic System based on Computational Intelligence – Evolutionary Generation of Regrasping Motion
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Toshio Fukuda, Yasuhisa Hasegawa
: pp. 443-449
Deep Fusion of Computational and Symbolic Intelligent Processing by Symbol Emergence
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Shun’ichi Tano
: pp. 450-456
Image Retrieval using Conceptual Fuzzy Sets
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Tomohiro Takagi, Kazushi Kawase and Kazuhiko Otsuka
: pp. 457-463
Topic-based Intelligent Support System for Information Retrieval
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Yasufumi Takama and Kaoru Hirota

No.5

(Sep)

Fuzzy Logic and Intelligence System

Fuzzy Logic and Intelligence System

: pp. 319-320
Fuzzy Logic and Intelligence System
Hyung Lee-Kwang and Ju-Jang Lee

These papers are originally published in the proceedings of Korea fuzzy logic and intelligent systems society (KFIS) fall conference in 1999. Eight papers are selected for this special issue. Major topics of them are fuzzy theory, neural network, inference system, intelligent controller, etc. In this issue, Seihwan Park and Hyung Lee-Kwang extend the concept of fuzzy hypergraph to type-2 fuzzy hypergraph using type-2 fuzzy sets. It has not only the same properties of hypergraphs but also the extended properties of them. It is also shown that interval valued fuzzy hypergraph is a special case of type-2 fuzzy hypergraph. Jung-Heum Yon, Yong-Taek Kim, Jae-Yong Seo and Hong-Tae Jeon design an efficient neural network called dynamic multidimensional wavelet neural network. It can perform an effective dynamic mapping with less dimensions of the input signal. These features show one way to compensate the weakness of the diagonal recurrent neural network and feedforward wavelet neural network. Yigon Kim, Yang Hee Jung and Young Chel Bae propose a new method for diagnosis of insulation aging using wavelet. It measures the partial discharge on-line from data acquisition system and analyses it using wavelet to acquire 21) patterns. They design a neuro-fuzzy model that diagnoses an electrical equipment using the data. Byung-Jae Choi, Seong-Woo Kwak and Byung Kook Kim develop an adaptive fuzzy logic controller. A sole input fuzzy variable is used to simplify the design procedure and the switching hyperplane of sliding mode control is used to improve the adaptability. Myung-Geun Chun, Keun-Chang Kwak and Jeong-Woong Ryu show an efficient fuzzy rule generation scheme for adaptive network-based fuzzy inference system using the conditional fuzzy c-means and fuzzy equalization methods. They apply this method to the truck backer-upper control and Box-Jenkins modeling problem. Daijin Kim proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Twostage classification method is used. All data are classified by using the lower approximation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. Min-Soeng Kim, Sun-Gi Hong and Ju-Jang Lee incorporate the Q-learning algorithm into the fuzzy logic controller. Modified fuzzy rule is used for the incorporation. As a result, a fuzzy logic controller is obtained that can learn through experience. Dong Hwa Kim designs a new 2-DOF PID controller and applies it to the operating data based transfer function of Gun-san Gas turbine in Korea. We hope that this issue can be helpful to readers and we appreciate professor Kaoru Hirota for his interest and support for the publication.

: pp. 321-326
Design of a Single-input Adaptive Fuzzy Logic Controller using a Switching Hyperplane
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Byung-Jae Choi, Seong-Woo Kwak, Byung Kook Kim
: pp. 327-335
IRIS Data Classification Using Tolerant Rough Sets1
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Daijin Kim, Sung-Yang Bang
: pp. 336-340
Dynamic Multidimensional Wavelet Neural Network and Its Application
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Jung-Heum Yon, Yong-Taek Kim, Jae-Yong Seo, and Hong-Tae Jeon
: pp. 341-348
New 2-DOF PID Controller Tuning by Adaptive Neural Fuzzy Inference System for Gas Turbine Control System
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Dong Hwa Kim and Chang Kee Jung
: pp. 349-354
Self-Learning Fuzzy Logic Controller using Q-Learning
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Min-Soeng Kim, Sun-Gi Hong and Ju-Jang Lee
: pp. 355-361
A Fuzzy Rule Extraction Method for ANFIS Using CFCM and Fuzzy Equalization
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Myung-Geun Chun, Keun-Chang Kwak, Jeong-Woong Ryu and Witold Pedrycz
: pp. 362-367
Type-2 Fuzzy Hypergraphs Using Type-2 Fuzzy Sets
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Seihwan Park and Hyung Lee-Kwang
: pp. 368-372
Design of Diagnosis System for Insulation Degradation by Using Neurofuzzy Model
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Yigon Kim, Yang Hee Jung and Yong Chul Bae
: pp. 373-379
Rule Reduction and Robust Control of Generalized Takagi-Sugeno Fuzzy Systems
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Tadanari Taniguchi and Kazuo Tanaka
: pp. 380-386
Fuzzy Flip-Flops and their Applications to Fuzzy Memory Element and Circuit Design using FPGA
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Shin-ichi Yoshida, Yasufumi Takama and Kaoru Hirota
: pp. 387-394
Support System for Multimedia Information Data Acquisition Based on Fuzzy Inference with a Fuzzy Shift
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Kabsuk Oh, Kaoru Hirota

No.4

(Jul)

Intelligent Engineering Systems

Intelligent Engineering Systems

: pp. 237-239
Intelligent Engineering Systems
Imre J. Rudas

The “information revolution” of our time affects our entire generation. While a vision of the “Information Society,” with its financial, legal, business, privacy, and other aspects has emerged in the past few years, the “traditional scene” of information technology, that is, industrial automation, maintained its significance as a field of unceasing development. Since the old-fashioned concept of “Hard Automation” applicable only to industrial processes of fixed, repetitive nature and manufacturing large batches of the same product1)was thrust to the background by keen market competition, the key element of this development remained the improvement of “Machine Intelligence”. In spite of the fact that L. A. Zadeh already introduced the concept of “Machine Intelligence Quotient” in 1996 to measure machine intelligence2) , this term remained more or less of a mysterious meaning best explicable on the basis of practical needs. The weak point of hard automation is that the system configuration and operations are fixed and cannot be changed without incurring considerable cost and downtime. Mainly it can be used in applications that call for fast and accurate operation in large batch production. Whenever a variety of products must be manufactured in small batches and consequently the work-cells of a production line should be quickly reconfigured to accommodate a change in products, hard automation becomes inefficient and fails due to economic reasons. In these cases, new, more flexible way of automation, so-called “Soft Automation,” are expedient and suitable. The most important “ingredient” of soft automation is its adaptive ability for efficiently coping with changing, unexpected or previously unknown conditions, and working with a high degree of uncertainty and imprecision since in practice increasing precision can be very costly. This adaptation must be realized without or within limited human interference: this is one essential component of machine intelligence. Another important factor is that engineering practice often must deal with complex systems of multiple variable and multiple parameter models almost always with strong nonlinear coupling. Conventional analysis-based approaches for describing and predicting the behavior of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too categorical in the sense that in the name of “modeling accuracy,” they try to describe all structural details of the real physical system to be modeled. This significantly increases the intricacy of the model and may result in huge computational burden without considerably improving precision. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction is, the more work must be invested for obtaining it. Another important component of machine intelligence is a kind of “structural uniformity” giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to that of the ready-to-wear industry, whose products can later be slightly modified in contrast to the custom-tailors’ made-to-measure creations aiming at maximum accuracy from the beginning. Machines carry out these later corrections automatically. This “learning ability” is another key element of machine intelligence. To realize the above philosophy in a mathematically correct way, L. A. Zadeh separated Hard Computing from Soft Computing. This revelation immediately resulted in distinguishing between two essential complementary branches of machine intelligence: Hard Computing based Artificial Intelligence and Soft Computing based Computational Intelligence. In the last decades, it became generally known that fuzzy logic, artificial neural networks, and probabilistic reasoning based Soft Computing is a fruitful orientation in designing intelligent systems. Moreover, it became generally accepted that soft computing rather than hard computing should be viewed as the foundation of real machine intelligence via exploiting the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality. Further research in the past decade confirmed the view that typical components of present soft computing such as fuzzy logic, neurocomputing, evolutionary computation and probabilistic reasoning are complementary and best results can be obtained by their combined application. These complementary branches of Machine Intelligence, Artificial Intelligence and Computational Intelligence, serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in journals and conference proceedings worldwide substantiates this statement. Three years ago, a new series of conferences in this direction was initiated and launched with the support of several organizations including the IEEE Industrial Electronics Society and IEEE Hungary Section in technical cooperation with IEEE Robotics & Automation Society. The first event of the series hosted by Bdnki Dondt Polytechnic, Budapest, Hungary, was called “19997 IEEE International Conference on Intelligent Engineering Systems ” (INES’97). The Technical University of Vienna, Austria hosted the next event of the series in 1998, followed by INES’99 held by the Technical University of Kosice, Slovakia. The present special issue consists of the extended and revised version of the most interesting papers selected out of the presentations of this conference. The papers exemplify recent development trends of intelligent engineering systems. The first paper pertains to the wider class of neural network applications. It is an interesting report of applying a special Adaptive Resonance Theory network for identifying objects in multispectral images. It is called “Extended Gaussian ARTMAP”. The authors conclude that this network is especially advantageous for classification of large, low dimensional data sets. The second paper’s subject belongs to the realm of fuzzy systems. It reports successful application of fundamental similarity relations in diagnostic systems. As an example failure detection of rolling-mill transmission is considered. The next paper represents the AI-branch of machine intelligence. The paper is a report on an EU-funded project focusing on the storage of knowledge in a corporate organizational memory used for storing and retrieving knowledge chunks for it. The flexible structure of the system makes it possible to adopt it to different SMEs via using company-specific conceptual terms rather than traditional keywords. The fourth selected paper’s contribution is to the field of knowledge discovery. For this purpose in the first step, cluster analysis is done. The method is found to be helpful whenever little or no information on the characteristics of a given data set is available. The next paper approaches scheduling problems by the application of the multiagent system. It is concluded that due to the great number of interactions between components, MAS seems to be well suited for manufacturing scheduling problems. The sixth selected paper’s topic is emerging intelligent technologies in computer-aided engineering. It discusses key issues of CAD/CAM technology of our days. The conclusion is that further development of CAD/CAM methods probably will serve companies on the competitive edge. The seventh paper of the selection is a report on seeking a special tradeoff between classical analytical modeling and traditional soft computing. It nonconventionally integrates uniform structures obtained from Lagrangian Classical Mechanics with other simple elements of machine intelligence such as saturated sigmoid transition functions borrowed from neural nets, and fuzzy rules with classical PID/ST, and a simplified version of regression analysis. It is concluded that these different components can successfully cooperate in adaptive robot control. The last paper focuses on the complexity problem of fuzzy and neural network approaches. A fuzzy rule base, be it generated from expert operators or by some learning or identification schemes, may contain redundant, weakly contributing, or outright inconsistent components. Moreover, in pursuit of good approximation, one may be tempted to overly assign the number of antecedent sets, thereby resulting in large fuzzy rule bases and much problems in computation time and storage space. Engineers using neural networks have to face the same complexity problem with the number of neurons and layers. A fuzzy rule base and neural network design, hence, have two important objectives. One is to achieve a good approximation. The other is to reduce the complexity. The main difficulty is that these two objectives are contradictory. A formal approach to extracting the more pertinent elements of a given rule set or neurons is, hence, highly desirable. The last paper is an attempt in this direction. References 1)C. W. De Silva. Automation Intelligence. Engineering Application of Artificial Intelligence. Vol. 7. No. 5. 471-477 (1994). 2)L. A. Zadeh. Fuzzy Logic, Neural Networks and Soft Computing. NATO Advanced Studies Institute on Soft Computing and Its Application. Antalya, Turkey. (1996). 3)L. A. Zadeh. Berkeley Initiative in Soft Computing. IEEE Industrial Electronics Society Newsletter. 41, (3), 8-10 (1994).

: pp. 240-245
ARTMAP Neural Networks for Multispectral Image Classification
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Norbert Kopco, Peter Sincak and Stanislav Kaleta
: pp. 246-250
Similarity Relations in Diagnosis Fuzzy Systems
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Ján Vascák and Ladislav Madarász
: pp. 251-257
System Architecture for Support of Knowledge Management
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Marek Paralic, Tomás Sabol and Marian Mach
: pp. 258-262
Cluster Analysis as a First Step in the Knowledge Discovery Process
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Andreas Rauber and Jan Paralic
: pp. 263-267
Approach to Scheduling Problem Solution in Production Systems Using the Multiagent System
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Frankoviè, B., Labátová S. and Budinská, I.
: pp. 268-278
Emerging Intelligent Technologies in Computer-Aided Engineering
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Lász1ó Horváth* and Imre J. Rudas**
: pp. 279-285
Simultaneous Optimization of the External Loop Parameters in an Adaptive Control Based on the Co-operation of Uniform Procedures
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József K. Tar, Imre J. Rudas, Ladislav Madarász and János F. Bitó
: pp. 286-293
Complexity Minimalization of Nonsingleton-based Fuzzy-Neural Network
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Kin-fong Lei, Péter Baranyi and Yeung Yam
: pp. 294-301
Learning M-of-N Concepts for Medical Diagnosis Using Neural Networks
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Yoichi Hayashi, Rudy Setiono and Katsumi Yoshida
: pp. 302-312
Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model
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Jianqiang Yi, Naoyoshi Yubazaki and Kaoru Hirota

No.3

(May)

Sampling Research on Advanced Computational Intelligence in Canada

Sampling Research on Advanced Computational Intelligence in Canada

: p. 187
Sampling Research on Advanced Computational Intelligence in Canada
Max Meng and Witold Pedrycz

The 1999 IEEE Canadian Conference on Electrical and computer Engineering (CCECE’99) was held from May 9 to 12, 1999, at the Shaw Conference Centre in Edmonton. The conference was a great success with over 380 papers presented and more than 400 peoples from 38 different countries presenting their recent research results. The area of Computational Intelligence was one of the vivid pursuits presented at the conference. Subsequently, we have been invited by the Editors-in-Chief of the Journal of Advanced Computational Intelligence to prepare a Special Issue of the Journal CCECE’99 conference. After a careful and strict peer review process, we have chosen six papers to be included in this special issue. They are selected from more than 20 papers submitted to this special issue, which are extended versions of the papers presented at the CCECE’99 conference in the areas of advanced computational intelligence. The papers fully reflect the breadth and diversity of conceptual and algorithmic facets of Computational Intelligence along with a spectrum of applications. We thank the authors and reviewers for doing an excellent job. We are grateful to Kaoru Hirota and Toshio Fukuda for making this selection of papers a part of the journal. We do hope the readers will enjoy this issue.

: pp. 188-194
Fuzzy Time-Series Model of Electric Power Consumption
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Kazuhiro Ozawa, ’Takahide Niimura and Tomoaki Nakashima
: pp. 195-199
Application of Fuzzy Control to a Riderless Bicycle
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Gérard Lachiver and Saïd Berriah
: pp. 200-205
Neural Network Based Power Flow Predictor
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S. Madan and K. E. Bollinger
: pp. 206-211
Classification of Volumetric Storm Cell Patterns
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M. Alexiuk, N. Pizzi, P C. Li and W. Pedrycz
: pp. 212-219
Fuzzy Logic Based Speed Control of an IPM Synchronous Motor Drive
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M. N. Uddin and M. A. Rahman
: pp. 220-229
An Efficient Neural Network Model for Path Planning of Car-like Robots in Dynamic Environment
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Simon X. Yang and Max Meng

No.2

(Mar)

Simulated Evolution and Learning

Simulated Evolution and Learning

: p. 129
Simulated Evolution and Learning
Xin Yao

Evolution and learning are two fundamental forms of adaptationl,2). Simulated evolution and learning refers to the study of techniques and methods inspired by Nature for solving complex and difficult real-world problems. These techniques and methods include evolutionary algorithms3), fuzzy learning algorithms, neural learning algorithms, and various statistical learning methods such as nearest neighbor classifiers. In addition to various learning tasks, these techniques and methods have also been applied to various difficult optimization problems that cannot be solved effectively by classical methods (such as mathematical programming methods). This special issue contains six papers selected from those presented at the Second Asia-Pacific Conference on Simulated Evolution And Learning (SEAL’98), Canberra, Australia, 24-27 November 1998. However, all six papers have been rereviewed and substantially extended and revised. They represent significant improved work from their original SEA L’98 papers. The six papers can be grouped into three categories. The first two papers by He et al. and by Ishibuchi and Nakashima described novel applications of genetic algorithms to nearest neighbor classifiers. The next two papers by Kawakami et al. and by Tachibana and Furuhashi presented new fuzzy learning systems. The last two papers by Myung and Kim and by Yu and Wu discussed constrained optimization using the evolutionary approach. I would like to take this opportunity to thank Dr Bob McKay, the SEAL’98 Organizing Committee Chair, for playing a pivotal role in organizing the very successful SEAL’98, Professor Kaoru Hirota, the Editor-in-Chief of the Journal of Advanced Computational Intelligence, for encouraging me to edit this special issue, and all the authors for their high-quality work. References: 1)X. Yao, J-H. Kim, and T. Furuhashi, eds., Simulated Evolution and Learning, Vol. 1285 of Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer-Verlag, 1997. 2)B. Mckay, X. Yao, C. S. Newton, J-H. kim, and T. Furuhashi, eds., Simulated Evolution and Learning, Vo1.1585 of Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer-Verlag, 1999. 3)X. Yao, ed., Evolutionary Computation: Theory and Applications. Singapore: World Scientific Publishing Co., 1999.

: pp. 130-137
Application of Genetic Algorithm and K-Nearest Neighbour Method in Real World Medical Fraud Detection Problem
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Hongxing He, Simon Hawkins, Warwick Graco and Xin Yao
: pp. 138-145
Pattern and Feature Selection by Genetic Algorithms in Nearest Neighbor Classification
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Hisao Ishibuchi and Tomoharu Nakashima
: pp. 146-151
A Reinforcement Learning Scheme of Fuzzy Rules with Reduced Conditions
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Hiroshi Kawakami, Osamu Katai and Tadataka Konishi
: pp. 152-157
Uneven Input Space Division and Balance of Generality and Conciseness of Submodels for Hierarchical Fuzzy Modeling
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Kanta Tachibana and Takeshi Furuhashi
: pp. 158-163
Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization
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Hyun Myung and Jong-Hwan Kim
: pp. 164-170
An Adaptive Penalty Function Method for Constrained Optimization with Evolutionary Programming
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Xinghuo Yu and Baolin Wu
: pp. 171-176
Knowledge Based Automated Boundary Detection for Qualifying of LV Function in Low Contrast Angiographic Images
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Yang Hee Yee , Chun Kee Jeon , Sang-Rok Oh and Mignon-Park
: pp. 177-184
Vehicles Dispatching Problem for Cooperative Deliveries from Multiple Depots
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Kewei Chen, Yasufumi Takama and Kaoru Hirota

No.1

(Jan)

Special Issue on Hybrid System

Special Issue on Hybrid System

: pp. 3-11
Visualization of Categorical Data by Hybridization of Two Types of Neural Networks
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Masahiro Tanaka and Hideki Fujiwara
: pp. 12-17
Fast Neuro-Classification of New and Used Bills Using Spectral Patterns of Acoustic Data
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Dongshik Kang, Sigeru Omatu and Michifumi Yoshioka
: pp. 18-23
Neuro-Classification of Currency Fatigue Levels Based on Acoustic Cepstrum Patterns
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Masaru Teranishi, sigeru Omatu and Toshihisa Kosaka
: pp. 24-30
A Rule Discovery by Fuzzy Classifier System Utilizing Symbolic Information
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Makoto Fujii and Takeshi Furuhashi
: pp. 31-38
Using Rough Sets for Practical Feature Selection in a Rough Sets/Neural Network Framework for Knowledge Discovery
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Ilona Jagielska
: pp. 39-45
Knowledge Processing System Using Chaotic Associative Memory
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Yuko Osana and Masafumi Hagiwara
: pp. 46-51
Procedural Knowledge Processing Based on Area Representation Using a Neural Network
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Seiya Fujinaga and Masafumi Hagiwara
: pp. 52-56
Application of Choquet Integral to Efficient Aggregation of Neural Networks
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Sung-Bae Cho
: pp. 57-65
The Application of Hybrid Evolving Connectionist Systems to Image Classification
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Nikola K. Kasabov, Steven A. Israel and Brendon J. Woodford
: pp. 66-75
Differentiation of the Choquet Integral and Its Application to Long-term Debt Ratings
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Toshihiro Kaino and Kaoru Hirota
: pp. 76-87
Temporal-Hierarchical Emergency-Degree Inference System for Running Vehicles Using Image and Navigation Data
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Satoshi Koizumi, Masayuki Matsushita, Yasufumi Takama, Hiroshi Takahashi and Kaoru Hirota
: pp. 88-94
A Genetic Algorithm for Machines Sequencing Considering Operation Flexibility and AGV Guidepath
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Chiung Moon and Mitsuo Gen
: pp. 95-101
Fuzzy Rule Interpolation by the Conservation of Relative Fuzziness
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Laszlo T. Koczy, Kaoru Hirota and Tamas D. Gedeon
: pp. 102-110
Representation and Propagation of Information Granules in Rule-based Computing
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Witold Pedrycz and George Vulcovich
: pp. 111-119
Non-network Type Artificial Immune System and its Application to Automated Guided Vehicle (AGV) System
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Ben T. Nohara and Hiroko Takahashi
: pp. 120-127
A New Approach To Speech Coding: the Neural Predictive Coding
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Bruno Gas, Jean Luc Zarader and Cyril Chavy

Vol.3 (1999)

No.6

(Dec)

Introduction to the Special Issue on Learning in Intelligent Algorithms and Systems Design

Introduction to the Special Issue on Learning in Intelligent Algorithms and Systems Design

: pp. 439-440
Introduction to the Learning in Intelligent Algorithms and Systems Design
Chengqi Zhang*, Ling Guan** and Zheru Chi***

Learning has long been and will continue to be a key issue in intelligent algorithms and systems design. Emulating the behavior and mechanisms of human learning by machines at such high levels as symbolic processing and such low levels as neuronal processing has long been a dominant interest among researchers worldwide. Neural networks, fuzzy logic, and evolutionary algorithms represent the three most active research areas. With advanced theoretical studies and computer technology, many promising algorithms and systems using these techniques have been designed and implemented for a wide range of applications. This Special Issue presents seven papers on learning in intelligent algorithms and systems design from researchers in Japan, China, Australia, and the U.S. Neural Networks: Emulating low-level human intelligent processing, or neuronal processing, gave birth of artificial neural networks more than five decades ago. It was hoped that devices based on biological neural networks would possess characteristics of the human brain. Neural networks have reattracted researchers’ attention since the late 1980s when back-propagation algorithms were used to train multilayer feed-forward neural networks. In the last decades, we have seen promising progress in this research field yield many new models, learning algorithms, and real-world applications, evidenced by the publication of new journals in this field. Fuzzy Logic: Since L. A. Zadeh introduced fuzzy set theory in 1965, fuzzy logic has increasingly become the focus of many researchers and engineers opening up new research and problem solving. Fuzzy set theory has been favorably applied to control system design. In the last few years, fuzzy model applications have bloomed in image processing and pattern recognition. Evolutionary Algorithms: Evolutionary optimization algorithms have been studied over three decades, emulating natural evolutionary search and selection so powerful in global optimization. The study of evolutionary algorithms includes evolutionary programming (EP), evolutionary strategies (ESs), genetic algorithms (GAs), and genetic programming (GP). In the last few years, we have also seen multiple computational algorithms combined to maximize system performance, such as neurofuzzy networks, fuzzy neural networks, fuzzy logic and genetic optimization, neural networks, and evolutionary algorithms. This Special Issue also includes papers that introduce combined techniques. Wang et al present an improved fuzzy algorithm for enhanced eyeground images. Examination of the eyeground image is effective in diagnosing glaucoma and diabetes. Conventional eyeground image quality is usually too poor for doctors to obtain useful information, so enhancement is required to eliminate this. Due to details and uncertainties in eyeground images, conventional enhancement such as histogram equalization, edge enhancement, and high-pass filters fail to achieve good results. Fuzzy enhancement enhances images in three steps: (1) transferring an image from the spatial domain to the fuzzy domain; (2) conducting enhancement in the fuzzy domain; and (3) returning the image from the fuzzy domain to the spatial domain. The paper detailing this proposes improved mapping and fast implementation. Mohammadian presents a method for designing self-learning hierarchical fuzzy logic control systems based on the integration of evolutionary algorithms and fuzzy logic. The purpose of such an approach is to provide an integrated knowledge base for intelligent control and collision avoidance in a multirobot system. Evolutionary algorithms are used as in adaptation for learning fuzzy knowledge bases of control systems and learning, mapping, and interaction between fuzzy knowledge bases of different fuzzy logic systems. Fuzzy integral has been found useful in data fusion. Pham and Wagner present an approach based on the fuzzy integral and GAs to combine likelihood values of cohort speakers. The fuzzy integral nonlinearly fuses similarity measures of an utterance assigned to cohort speakers. In their approach, Gas find optimal fuzzy densities required for fuzzy fusion. Experiments using commercial speech corpus T146 show their approach achieves more favorable performance than conventional normalization. Evolution reflects the behavior of a society. Puppala and Sen present a coevolutionary approach to generating behavioral strategies for cooperating agent groups. Agent behavior evolves via GAs, where one genetic algorithm population is evolved per individual in the cooperative group. Groups are evaluated by pairing strategies from each population and best strategy pairs are stored together in shared memory. The approach is evaluated using asymmetric room painting and results demonstrate the superiority of shared memory over random pairing in consistently generating optimal behavior patterns. Object representation and template optimization are two main factors affecting object recognition performance. Lu et al present an evolutionary algorithm for optimizing handwritten numeral templates represented by rational B-spline surfaces of character foreground-background-distance distribution maps. Initial templates are extracted from training a feed-forward neural network instead of using arbitrarily chosen patterns to reduce iterations required in evolutionary optimization. To further reduce computational complexity, a fast search is used in selection. Using 1,000 optimized numeral templates, the classifier achieves a classification rate of 96.4% while rejecting 90.7% of nonnumeral patterns when tested on NIST Special Database 3. Determining an appropriate number of clusters is difficult yet important. Li et al based their approach based on rival penalized competitive learning (RPCL), addressing problems of overlapped clusters and dependent components of input vectors by incorporating full covariance matrices into the original RPCL algorithm. The resulting learning algorithm progressively eliminates units whose clusters contain only a small amount of training data. The algorithm is applied to determine the number of clusters in a Gaussian mixture distribution and to optimize the architecture of elliptical function networks for speaker verification and for vowel classification. Another important issue on learning is Kurihara and Sugawara’s adaptive reinforcement learning algorithm integrating exploitation- and exploration-oriented learning. This algorithm is more robust in dynamically changing, large-scale environments, providing better performance than either exploitation- learning or exploration-oriented learning, making it is well suited for autonomous systems. In closing we would like to thank the authors who have submitted papers to this Special Issue and express our appreciation to the referees for their excellent work in reading papers under a tight schedule.

: pp. 441-445
Enhancement of Eyeround Images Based on an Improved Fuzzy Algorithm
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Yaming Wang, Jiajun Wang, Yuanmei Wang and Yude Dong
: pp. 446-450
Design of Self-Learning Hierarchical Fuzzy Logic for Guidance and Control of Multirobot Systems
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Masoud Mohammadian
: pp. 451-456
Speaker Verification with Fuzzy Fusion and Genetic Optimization
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Tuan Pham and Michael Wagner
: pp. 457-461
Evolving Cooperative Groups Using Shared Memory
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Narendra Puppala and Sandip Sen
: pp. 462-466
An Evolutionary Algorithm for Optimizing Handwritten Numeral Templates Represented by Rational B-Spline Surfaces
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Zheru Chi, Zhongkang Lu, Wan-chi Siu and Peng-Fei Shi
: pp. 467-473
Determining the Optimal Number of Clusters by an Extended RPCL Algorithm
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Xin Li, Man Wai Mak and Chi Kwong Li
: pp. 474-478
Adaptive Reinforcement Learning Integrating Exploitation-and Exploration-oriented Learning
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Satoshi Kurihara, Rikio Onai and Toshiharu Sugawara
: pp. 479-484
Emotional Robotic System with Psychological Model
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Hidenori Ishihara and Toshio Fukuda
: pp. 485-490
Probabilistic Learning-Network-Based Robust Control Scheme for Nonlinear Systems
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Jinglu Hu, Kotaro Hirasawa, Junichi Murata, Chunzhi Jin and Takuya Matsuoka
: pp. 491-498
Realization of PID Control by Fuzzy Inference and its Application to Hybrid Control
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Kenichiro Hayashi, Akifumi Otsubo and Kazuhiko Shiranita
: pp. 499-508
Fuzzy Sets of Type-2
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Robert John
: pp. 509-514
Null Queries with Compound Fuzzy Attributes
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Shyue-Liang Wang and Yu-Jane Tsai
: pp. 515-518
Fuzzy Control of a Direct Current Motor System and Stability Analysis
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Euntai Kim, Heejin Lee and Dongyon Kim
: pp. 519-523
Narrowing Algorithm for Indoor-Air Pollutants using Gas Sensor Patterns
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Takashi Oyabu, Takeshi Onodera, Hidetaka Nambo and Haruhiko Kimura
: pp. 524-531
Neurofuzzy Approach to Fault Detection of Nonlinear Systems
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Jinglu Hu, Kotaro Hirasawa and Kousuke Kumamaru
: pp. 532-540
Quality Function for Unsupervised Classification and its Use in Graphic Arts
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Adas Gelzinis, Antanas Verikas and Kerstin Malmgvist

No.5

(Oct)

Rule Extraction from Data

Rule Extraction from Data

: pp. 339-340
Rule Extraction from Data
Takeshi Furuhashi

Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This special issue entitled”Rule Extraction from Data” is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.

: pp. 341-347
Rule Extraction by Structural Learning with an Immediate Critic
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Masumi Ishikawa
: pp. 348-356
Hybrid Neural-global Minimization Method of Logical Rule Extraction
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Wlodzislaw Duch, Rafal Adamczak, KrzysAof Grabczewski and Grzegorz Zal
: pp. 357-367
Fuzzy Rule Acquisition from Trained Artificial Neural Networks
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Peter Geczy, Shiro Usui
: pp. 368-372
A New Framework for Fuzzy Modeling Using Genetic Algorithm
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Seiichi Matsushita, Takeshi Furuhashi and Hiroaki Tsutsui
: pp. 373-385
Fuzzy Modeling with Local and Global Objectives
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John Yen and Wayne Gillespie
: pp. 386-393
Linguistic Rule Extraction from Numerical Data for High-dimensional Classification Problems
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Hisao Ishibuchi, Tadahiko Murata and Tomoharu Nakashima
: pp. 394-400
Assessing the Relevance of Processing Building Blocks in Evolutionary Computation: Experiments with Linear Systems of Equations
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David B. Fogel and Peter J. Angeline
: pp. 401-408
Dynamic Service Identification in A Distributed Environment
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E.Damiani and M.G.Fugini
: pp. 409-417
Fuzzy Cognitive Map Approach to Process Control Systems Chrysostomos
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D. Stylios, Peter P Groumpos and Voula C. Georgopoulos
: pp. 418-426
Coevolutionary Algorithms for Realization of Intelligent Systems
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Hyo-Byung Jun and Kwee-Bo Sim
: pp. 427-430
Neural Network-Based Speed Control of A Two-Mass-Model System
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Rached Dhaouadi, Khaled Nouri
: pp. 431-438
Improvement of Control Performance for Low-Dimensional Number of Fuzzy Labeling Using Simplified Inference
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Kenichiro Hayashi, Akifumi Otsubo and Kazuhiko Shiranita

No.4

(Aug)

Professor Ernest Czogala Memorial Issue Part 2

Professor Ernest Czogala Memorial Issue Part 2

: p. 213
Professor Ernest Czogala Memorial Issue Part 2
Yoichi Hayashi

After the publication of Professor Ernest Czogala Memorial Issue Part 1, I found 2 obituaries, one in the International Journal of Applied Mathematics and Computer Science, Vo1.9 No.1, published by the Technical University Press, Poland, by Maria Drelichowska and Lucjan Karwan of the Technical University, Silesia, Gliwice, Poland. The other was published”Fuzzy Systems in Medicine” (P.S. Szczepaniak eds.), Physica-Verlag, published in January 2000, which included my paper,”Fuzzy and Crisp Logical Rule Extraction Methods in Application to Medical Data,” which I wrote in collaboration with W. Duch et al. This issue includes the final paper,”A Classifier Based on Neurofuzzy Inference,” by Ernest Czogala, Jacek Leski, and Yoichi Hayashi, which is why this edition has been published later than planned. I would like include a comment by Samuel Ullman, written in Birmingham, Alabama, where Ernest’s cousin and researcher James J. Buckley works: “Youth is not a time of life; it is a state of mind… Nobody grows old merely by years. We grow old by deserting our ideals.” The Samuel Ullman Museum at the University of Alabama is at 2150 15th Avenue South, Birmingham, Alabama 35294-1150 (phone:001(205)934-5634), thanks to citizens of Japan and the United States. We thank Drs. Toshio Fukuda and Kaoru Hirota, Editors in Chief of the JACI, for accepting my proposal for this special issue. Special thanks also go to the referees for their cooperation, devotion, and review. We also thank Fuji Technology Press President Keiji Hayashi for his dedicated work.

: pp. 215-222
Knowledge Extraction from Unknown Environment by Artificial-life Approach
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Ryoji Sawa, Yuji Makita and Masafumi Hagiwara
: pp. 223-233
Emergence of Learning Rule in Neural Networks Using Genetic Programming Combined with Decision Trees
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Noboru Matsumoto, Kenneth J. Mackin and Eiichiro Tazaki
: pp. 234-244
Temporal Event Association and Output-Dependent Learning: A Proposed Scheme of Neural Molecular Connections
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Yukifumi Shigematsu, Hiroshi Okamoto, Kazuhisa Ichikawa and Gen Matsumoto
: pp. 245-254
Fuzzy Neural Models Based on Some New Fuzzy* Arithmetic Operations
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Petr Musilekl and Madan M. Gupta
: pp. 255-263
Conceptual Fuzzy Matching and the Realization of Search Agents
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Tomohiro Takagi, Shinichi Kasuya, Masao Mukaidono and Toru Yamaguchi
: pp. 264-273
Theories of Set and Logic with Crisp or Fuzzy Information Granules
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I.B. Turksen
: pp. 274-281
Car Type/Name Recognition System Based on The Concept of Fixation
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Yoshinori Arai, Kaoru Hirota
: pp. 282-288
A Classifier Based on Neuro-Fuzzy Inference System Ernest
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Czogala, Jacek Leski, and Yoichi Hayashi
: pp. 289-298
Compensatory Multicriteria Aggregation Algorithm
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Marcelo Godoy Simoes
: pp. 299-302
SIRMs Connected Fuzzy Inference Model Applied to Process Control — Automatic Tuning Using a Genetic Algorithm Carla Cavalcante
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Koike and Kaoru Hirota
: pp. 303-311
LimNet-Flexible Learning Network Containing Linear Properties
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Jinglu Hu, Kotaro Hirasawa and Kousuke Kumamaru
: pp. 312-319
Control of Decentralized Systems Based on Nash Equilibrium Concept of Game Theory
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Kotaro Hirasawa, Jinglu Hu, Yusuke Yamamoto, Chunzhi Jin and Yurio Eki
: pp. 320-325
Moving Genetic Algorithm Based Fuzzy Modeling
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Euntai Kim, Heejin Lee, Chang-Hoon Lee, Jung-Hwan Kim
: pp. 326-331
Local Representation Neural Networks for Feature Selection
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M. Mar Abad Grau and L. Daniel Hernandez Molinero
: pp. 332-338
Tracking Control of Variable Structure Using Fuzzy Variable Boundary Layer
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Heejin Lee, Dong-Yon Kim, Taeck-Kie Lee, Sang-Hoon Kim, and Mignon Park

No.3

(Jun)

Professor Ernest Czogala Memorial Issue Part 1

Professor Ernest Czogala Memorial Issue Part 1

: pp. 149-150
Professor Ernest Czogala Memorial Issue Part 1
Yoichi Hayashi

As you may know from recent e-mail, BUSEFAL Vo1.76, Obituary, and Fuzzy Sets and Systems (FSS), Vo1.104, No.2, Obituary, Prof. Ernest Czogala passed away on October 8, 1998. First, I would like to express my sincere condolences to his eldest daughter Theresa Czogala-Koczy and son. The call for papers for this special issue has been answered from all over the globe. This issue includes the first seven accepted. The next issue will include those remaining. Since a formal obituary was provided by Professor Witold Pedrycz, a student of Ernest, in FSS as indicated above, I will add a few words of my own here. I first met Prof. Czogala when I was a visiting professor at the University of Alabama at Birmingham (UAB) Department of Computer and Information Sciences in 1990-1991. He was visiting the Mathematics Department, working with Professor James J. Buckley. When I first met Ernest, I intuitively felt that he, Jim (Prof. Buckley), and I would accomplish outstanding work in the near future. I promised to invite Ernest and Jim to Ibaraki University Department of Computer and Information Sciences in summer 1991. After my sabbatical at UAB, I received a letter from Ernest, asking, “Could I really visit your university for a month?” I immediately sent a formal invitation letter to Ernest and Jim. Ernest purchased an airline ticket between Poland and Japan – a princely sum equal to 6 months of his salary! He arrived August 19, 1991, at Narita Airport around 07:30 aboard All Nippon Airways from Poland via Wien. I drove a Toyota Camry to Narita from Hitachi City, Ibaraki Prefecture – a 2.5-hour excited jaunt. My wife Madoka and I went to pick him up. He appeared easygoing, shod in flip-flops. Madoka and I took him to Narita-san Temple – and found he had only 50 DM with him! After spending a few hours at Narita-san and eating lunch, Jim arrived from Birmingham. We picked him up and returned to Hitachi. Ibaraki University has a very old-fashioned guesthouse – but it only cost 700 a day. The next day, a coup d’etat shook Moscow. Ernest applauded, saying “Poland will be free.” From that day, we started our cooperative research in an un-air-conditioned office at Ibaraki University Department of Computer and Information Sciences. Outside, it was over 35°C, which made it 38°C in the office. During his stay, we wrote many papers. One recently appeared carefully selected based on the Science Citation Index, “On the Equivalence of Neural Nets and Fuzzy Expert Systems,” FSS, Vol. 100 Supplement, pp. 145-150 (1999). This short proof paper was outstanding. The original paper was submitted to Professor H.-J. Zimmermann, Founding Editor of FSS, in 1991. The paper was soon revised, accepted for publication in 1992, and published January 25, 1993. We have published many journals and refereed International Conference papers. I recollect papers worked on with Ernest. They include: 1. Hayashi, Y, Buckley, J.J. and Czogala, E., Systems Engineering Applications of Fuzzy Neural Networks, Journal of Systems Engineering, Vol.2, pp.232-236 (1992). 2. Buckley, J.J., Hayashi, Y and Czogala, E., On the Equivalence of Neural Nets and Fuzzy Expert Systems, Fuzzy Sets and Systems, Vo1.53, No.2, pp.129-134 (1993). 3. Hayashi, Y, Buckley, J.J. and Czogala, E., Fuzzy Neural Network with Fuzzy Signals and Weights, International Journal of Intelligent Systems, Vol.8, pp.527-537 (1993). In 1995, Ernest came to Yokohama, Japan, to present his paper at FUZZIEEE/IFES’95. Madoka, my 2-year-old son Yuichiro and I met him and ate Chinese lunch together. We promised to meet again. In 1997, I met him again at Barcelona for FUZZ-IEEE’97. He asked about the amount of grants I had applied for. I replied 700 million for three years. “That’s beyond all belief!” Unfortunately, my request was not met… Ernest, Jim, and I would have liked to have a delicious beer commemorating the FSS Vo1.100 Supplement. But although we never got to share that beer, the papers – the fruit of great efforts – never died. I believe our publication will continue to be referenced by many researchers all over the world. The FSS paper also symbolizes the position of associate professor for me at Ibaraki University. I was 34 years old at the time. Those of you wanting to get in touch with Ernest’s oldest daughter are asked to send inquiries to the following address: Theresa Czogala-Koczy ul. Kochanowskiego 29-15 44-100 Gliwice, Poland We thank Drs. Toshio Fukuda and Kaoru Hirota, Editors in Chief of the JACI, for accepting my proposal for this special issue. Special thanks also go to the referees for their kind cooperation, devotion, and rigorous review! We also thank Mr. Yasushi Inoue for his excellent management of the editorial work.

: pp. 151-157
Fuzzy Set Based Neural Networks: Structure, Learning and Application
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Walmir Caminhas, Hermano Tavares, Fernando Gomide and Witold Pedrycz
: pp. 158-163
On the Optimization of Fuzzy-Controllers by Neural Networks
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Wolfram-M. Lippe, Steffen Niendieck, Andreas Tenhagen
: pp. 164-170
Fuzzy Geometry Database
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James J. Buckley, Thomas Feuring and Yoichi Hayashi
: pp. 171-176
Solving Fuzzy Problems in Operations Research
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James J. Buckley, Thomas Feuring and Yoichi Hayashi
: pp. 177-185
Neuro-Fuzzy Systems Approaches
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Danuta Rutkowska*, Yoichi Hayashi**
: pp. 186-192
Behavior Generation for Mobile Robot Coordinating Simple Behavior
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Yasuhisa Hasegawa and Toshio Fukuda
: pp. 193-199
Knowledge-Based Approach in Intelligent Handwritten Form Processing
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Liya Ding and Ho Chung Lui
: pp. 200-206
Self-Tuning for Fuzzy Rule Generation Based upon Fuzzy Singleton-type Reasoning Method
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Yan Shi and Masaharu Mizumoto
: pp. 207-210
Utility Revision Mechanism Based on User’s Subjective Decision Hierarchy for Multiagent-Based Group Decision Support
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Takayuki Ito and Toramatsu Shintani

No.2

(Apr)

Advanced Computational Intelligence in Control Theory and Applications

Advanced Computational Intelligence in Control Theory and Applications

: p. 67
Advanced Computational Intelligence in Control Theory and Applications
Kazuo Tanaka

We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a “soft computing” technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

: pp. 68-74
Nonlinear Model Following Control via Takagi-Sugeno Fuzzy Model
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Tadanari Taniguchi and Kazuo Tanaka
: pp. 75-81
Evolutionary Strategy Using Statistical Information and Its Application to Mobile Robot Control
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Kiyotaka Izumi, Keigo Watanabe, and M.M.A. Hashem
: pp. 82-98
Computational Intelligence for Robust Control Algorithms of Complex Dynamic Systems with Minimum Entropy Production
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S.V. Ulyanov, K. Yamafuji, V.S. Ulyanov, 1. Kurawaki, T. Hagiwara and S.A. Panfilov
: pp. 99-105
Fuzzy Control Stability Analysis Using a Generalized Fuzzy Petri Net Model
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Takeshi Furuhashi, Hidehiro Yamamoto, James F. Peters and Witold Pedrycz
: pp. 106-113
Hierarchical Fuzzy Intelligent Controller for Gymnastic Bar Actions
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