Title :
Determination of antecedent structure for fuzzy modeling using genetic algorithm
Author :
Matsushita, S. ; Kuromiya, A. ; Yamaoka, M. ; Furuhashi, T. ; Uchikawa, Y.
Author_Institution :
Nagoya Municipal Ind. Res. Inst., Japan
Abstract :
Fuzzy modeling is one of the promising methods for describing nonlinear systems. Determination of antecedent structures of fuzzy models, i.e. input variables and number of membership functions for the inputs has been one of the most important problems of the fuzzy modeling. This paper presents a new method to find proper structures in the antecedent for fuzzy modeling of nonlinear systems using genetic algorithm. The new method is effective to identify precise fuzzy models of systems with strong nonlinearities. A simulation is done to show the effectiveness of the proposed method
Keywords :
fuzzy logic; genetic algorithms; inference mechanisms; nonlinear systems; antecedent structure; fuzzy modeling; genetic algorithm; input variables; membership functions; Abstracts; Degradation; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; Input variables; Nonlinear systems; Numerical simulation;
Conference_Titel :
Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
Conference_Location :
Nagoya
Print_ISBN :
0-7803-2902-3
DOI :
10.1109/ICEC.1996.542367
