Evolutionary approach for the beta function based fuzzy systems

Author

Aouiti, Chaouki ; Alimi, Adel M. ; Karray, Fakhreddine ; Maalej, Aref

Author_Institution

Fac. of Sci. of Bizerta, Univ. of 7 November, Bizerta, Tunisia

Volume

1

fYear

2003

Firstpage

179

Abstract

We propose an evolutionary method for the design of Beta fuzzy systems (BFS). Classical training algorithms start with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking, the fuzzy system created is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BFS. In order to examine the performance of the proposed algorithm, it is used for the identification of an induction machine fuzzy plant model. The results obtained have been encouraging.

Keywords

asynchronous machines; fuzzy logic; fuzzy neural nets; fuzzy systems; genetic algorithms; identification; learning (artificial intelligence); radial basis function networks; Sugeno type fuzzy system; beta basis function neural network; beta function based fuzzy systems; crossover operators; evolutionary method; fuzzy plant model identification; fuzzy rules; hierarchical genetic learning model; induction machine; membership functions; optimal system structure; training algorithms; Algorithm design and analysis; Chaos; Design methodology; Fuzzy logic; Fuzzy systems; Genetic algorithms; Induction machines; Input variables; Mechanical engineering; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on

Print_ISBN

0-7803-7810-5

Type

conf

DOI

10.1109/FUZZ.2003.1209358

Filename

1209358