Rule extraction for fuzzy modeling

Abstract In this paper, a method based on genetic algorithms is proposed to automatically extract fuzzy rules to identify a system where only its input-output data are available. This method can determine a fuzzy system with fewer fuzzy rules as well as the antecedent and consequent parameters of the fuzzy rules at the same time. A nonlinear system is utilized to illustrate the efficiency of the proposed method in the rule extraction for fuzzy modeling.

[1]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Ching-Chang Wong,et al.  Switching-type fuzzy controller design by genetic algorithms , 1995, Fuzzy Sets Syst..

[6]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[7]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[8]  C. L. Karr,et al.  Fuzzy control of pH using genetic algorithms , 1993, IEEE Trans. Fuzzy Syst..

[9]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[10]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[11]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[12]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

[13]  David E. Goldberg,et al.  Control system optimization using genetic algorithms , 1992 .