Interpretable Fuzzy Rules Acquisition of Coupled System Using Interactive Genetic Algorithms
Dun-Yong Lu* and Takehisa Onisawa**
*School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, P. R. China
**Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
This paper describes the use of the interactive genetic algorithms to acquire fuzzy control rules with good interpretability for a complex system having dependent variables and non-linear property. A chromosome is coded with integer to represent a fuzzy rule, and individuals composed of various numbers of chromosomes are evolved by GA operations. The acquired fuzzy rules are explained with the linguistic expressions for fuzzy sets. These linguistic expressions are determined through comparing with the standard fuzzy sets of linguistic variables designated in advance. To reduce human fatigue in the individual evaluation process, only quantitative evaluation with fitness functions is given at earlier stage. When a so-called better individual appears, not only quantitative evaluation but qualitative one is used to evaluate both the interpretability and control performance of the acquired fuzzy rules. The presented approach is applied to the control of the coupled system having two control objectives with multi-input/output variables. Simulation experiments show that the approach is feasible to acquire the satisfactory fuzzy rules with good interpretability and good control performance.
-  Y. Jin and B. Sendhoff, “Extracting interpretable fuzzy rules from RBF Neural Networks,” Neural Processing Letter, Vol.17, No.2, pp. 149-164, 2003.
-  T. Suzuki, T. Furuhashi, and H. Tsutsui, “Evolutionary algorithm based fuzzy modeling using conciseness measure,” Proc. of Joint 9th IFSA Congress and 20th NAFIPS Int’l Conf., pp. 1575-1580, 2001.
-  L. Sanchez, “A fast genetic method for inducing linguistically understandable fuzzy models,” Proc. of Joint 9th IFSA Congress and 20th NAFIPS Int’l Conf., pp. 1559-1563, 2001.
-  H. Ishibuchi and T. Nakashima, “Three-objective genetics-based machine learning for linguistic rule extraction,” Information Science, Vol.136, pp. 109-133, 2001.
-  H. Roubos and M. Setnes, “Compact and transparent fuzzy models and classifiers through iterative complexity reduction,” IEEE Transactions on Fuzzy Systems, Vol.9, pp. 516-524, 2001.
-  D. Chakraborty and N. R. Pal, “Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm,” IEEE Transactions on Systems, Man, Cybernetics, Vol.B-31, pp. 391-400, 2001.
-  B. Wu and X. Yu, “Fuzzy modeling and identification with genetic algorithm based learning,” Fuzzy Sets and System, Vol.113, pp. 351-365, 2000.
-  M. Setnes, R. Babuska, U. Kaymak, and H. R. van Nauta Lemke, “Similarity measures in fuzzy rule base simplification,” IEEE Trans. Syst., Man, Cybern-Part B, Vol.28, pp. 376-386, 1998.
-  R. Guglielmann and L. Ironi, “The need for qualitative reasoning in fuzzy modeling: robustness and interpretability issues,” 18th International Workshop on Qualitative Reasoning, Northwestern University, Evanston, Illinois, USA, August 2-4, 2004.
-  T. Onisawa, “Soft computing in human centered systems thinking,” Lecture Notes in Artificial Intelligence 3558, V. Torra, Y. Narukawa, S. Miyamoto (Eds.), Modeling Decisions for Artificial Intelligence, pp. 36-46, 2005.
-  H. Takagi, “Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation,” Proc. of the IEEE, Vol.89, No.9, pp. 1275-1296, 2001.
-  Y. Dote and S. J. Ovaska, “Industrial Applications of Soft Computing: A Review,” The Special Issue on Industrial Innovation Using Soft Computing, Proc. of the IEEE, September, 2001.
-  S. Mitra, S. K. Pal, and P. Mitra, “Data mining in soft computing framework: A survey,” IEEE Transactions on Neural Networks, Vol.13, No.1, pp. 3-14, 2002.
-  M. Ohsaki and H. Takagi, “An input method using discrete fitness values for interactive GA,” J. of intelligent and fuzzy systems, Vol.6, pp. 131-145, 1998.
-  M. Naao and M. Yamamoto, “Evaluation of the image retrieval system using interactive genetic algorithm,” J. of Japanese society for artificial intelligence, Vol.13, No.5, pp. 720-727, 1998.
-  T. Ingu and H. Takagi, “Accelerating a GA convergence by fitting a single-peak function,” IEEE int. conf. on fuzzy systems, pp. 1415-1420, August 1999.
-  Y. Ishino and T. Terano, “Marketing data analysis using simulated breeding and inductive learning techniques,” J. of Japanese society for artificial intelligence, Vol.12, No.1, pp. 121-131, 1997.
-  M. Tabuchi and T. Taura, “Methodology for interactive knowledge acquisition between genetic learning engine and human,” J. of Japanese society for artificial intelligence, Vol.11, No.4, pp. 600-607, 1996.
-  Y. Jin, “Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement,” IEEE Transactions on Fuzzy Systems, Vol.8, No.2, pp. 212-221, 2000.
-  X. Wang, “Genetic algorithms: theory and applications,” Xian Jiaotong University Publishing House, pp. 18-50, 2002.
-  H. Ishibuchi and T. Yamamoto, “Fuzzy rule selection by multiobjective genetic local search algorithms and rule evaluation measures in data mining,” Fuzzy Sets and Systems, Vol.141, No.1, pp. 59-88, January 2004.
-  Y. Jin, W. V. Seelen, and B. Sendhoff, “On Generating FC3 Fuzzy Rule Systems from Data Using Evolution Strategies,” IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, Vol.29, No.6, pp. 829-845, 1999.
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