Genetic-based new fuzzy reasoning models with application to fuzzy control

Author

Park, Daihee ; Kandel, Abraham ; Langholz, Gideon

Author_Institution

Dept. of Comput. Sci., Korea Univ., Chochiwon, South Korea

Volume

24

Issue

1

fYear

1994

fDate

1/1/1994 12:00:00 AM

Firstpage

39

Lastpage

47

Abstract

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown in this paper that the performance of fuzzy control systems may be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate an optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller

Keywords

fuzzy control; fuzzy logic; genetic algorithms; inference mechanisms; fuzzy control; fuzzy membership functions; genetic-based fuzzy reasoning models; genetic-based learning mechanism; random selection; subjective selection; Computer science; DC motors; Fuzzy control; Fuzzy logic; Fuzzy reasoning; Fuzzy systems; Genetic algorithms; Humans; Learning systems; Process control;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.259684

Filename

259684