Title :
Efficient fuzzy modeling and evaluation criteria
Author :
Matsushita, S. ; Furuhashi, T. ; Tsutsui, H. ; Uchikawa, Y.
Author_Institution :
Nagoya Municipal Ind. Res. Inst., Japan
Abstract :
Hierarchical fuzzy modeling using fuzzy neural networks (FNN) is one of the effective approaches for modeling of nonlinear systems. Decision of antecedent structures of fuzzy models of nonlinear systems is made possible by a combination of FNN and genetic algorithm (GA). The disadvantage of this fuzzy modeling method is that the learning of FNN is time consuming. This paper presents an efficient fuzzy modeling method using simple fuzzy inference. The results of fuzzy modeling are heavily dependent on evaluation criteria. This paper also studies effects of evaluation criteria for the decision of the antecedent structure. Numerical experiments are done
Keywords :
fuzzy neural nets; genetic algorithms; inference mechanisms; modelling; nonlinear systems; antecedent structure; evaluation criteria; fuzzy modeling; fuzzy neural networks; genetic algorithm; learning; nonlinear systems; Abstracts; Data handling; Electronics industry; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; Industrial electronics; Input variables; Nonlinear systems;
Conference_Titel :
Knowledge-Based Intelligent Electronic Systems, 1997. KES '97. Proceedings., 1997 First International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-3755-7
DOI :
10.1109/KES.1997.616921