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Selection of input variables of fuzzy model using genetic algorithm with quick fuzzy inference

Authors :
H. Tsutsui
S. Matsushita
Takeshi Furuhashi
Yoshiki Uchikawa
Source :
Lecture Notes in Computer Science ISBN: 9783540633990, SEAL
Publication Year :
1997
Publisher :
Springer Berlin Heidelberg, 1997.

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.

Details

ISBN :
978-3-540-63399-0
ISBNs :
9783540633990
Database :
OpenAIRE
Journal :
Lecture Notes in Computer Science ISBN: 9783540633990, SEAL
Accession number :
edsair.doi...........9fc3fbc1d5b1cf4ad4e8cf6159cb4697
Full Text :
https://doi.org/10.1007/bfb0028520