Evolutionary design of fuzzy rule base for nonlinear system modeling and control

Author

Kang, Sin-Jun ; Chun-Hee Woo ; Hwang, Hee-Soo ; Woo, Chun-Hee

Author_Institution

Sch. of Electr. Eng., Yonsei Univ., Seoul, South Korea

Volume

8

Issue

1

fYear

2000

fDate

2/1/2000 12:00:00 AM

Firstpage

37

Lastpage

45

Abstract

In designing fuzzy models and controllers, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious trial-and-error process. The paper presents an approach to the evolutionary design of an optimal fuzzy rule base for modeling and control. Evolutionary programming is used to simultaneously evolve the structure and the parameter of fuzzy rule base for a given task. To check the effectiveness of the suggested approach, four numerical examples are examined. The performance of the identified fuzzy rule bases is demonstrated

Keywords

control system synthesis; evolutionary computation; fuzzy control; inference mechanisms; nonlinear control systems; parameter estimation; uncertainty handling; evolutionary design; evolutionary programming; fuzzy rule base; Calculus; Control system synthesis; Design optimization; Fuzzy control; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic programming; Nonlinear control systems; Nonlinear systems;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.824766

Filename

824766