Generating fuzzy rules by a GA-based method from input-output data

Author

Wong, Ching-Chang ; Che, Chia-Chong

Author_Institution

Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan

Volume

5

fYear

1999

fDate

1999

Firstpage

278

Abstract

A method based on the concepts of the genetic algorithm (GA) and recursive least-squares method is proposed to construct a fuzzy system directly from some gathered input-output data of the discussed problem. The proposed method can find an appropriate fuzzy system with fewer rules to approach an identified system under the condition that the constructed fuzzy system must satisfy a predetermined acceptable performance. In this method, each individual in the GA is constructed to determine the number of fuzzy rules and the premise part of the fuzzy system, and the recursive least-squares method is used to determine the consequent part of the constructed fuzzy system described by this individual. Finally, two identification problems of nonlinear systems are utilized to illustrate the efficiency of the proposed method

Keywords

fuzzy set theory; fuzzy systems; genetic algorithms; least squares approximations; nonlinear systems; recursive estimation; consequent part; fuzzy rules; identification problems; input-output data; premise part; recursive least-squares method; Data mining; Fuzzy sets; Fuzzy systems; Genetic algorithms; Input variables; Nonlinear systems; Parameter estimation; Shape; System identification; System performance;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.815561

Filename

815561