Fuzzy Logic and Intelligence System
Hyung Lee-Kwang and Ju-Jang Lee
Department of Electrical Engineering & Computer Science Korea Advanced Institute of Science and Technology373-1 Kusong-dong Yusong-gu, Taejon, 305-701 Republic of Korea
Published:September 20, 2000
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.
Cite this article as:H. Lee-Kwang and J. Lee, “Fuzzy Logic and Intelligence System,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.5, pp. 319-320, 2000.Data files: