Hierarchical genetic algorithms for fuzzy system optimization in intelligent control

Author

Castillo, Oscar ; Lozano, Antonia ; Melin, Patricia

Author_Institution

Dept. of Comput. Sci., Tijuana Inst. of Technol., Mexico

Volume

1

fYear

2004

fDate

27-30 June 2004

Firstpage

292

Abstract

We describe in this paper the use of hierarchical genetic algorithms for fuzzy system optimization in intelligent control. In particular, we consider the problem of optimizing the number of rules and membership functions using an evolutionary approach. The hierarchical genetic algorithm enables the optimization of the fuzzy system design for a particular application. We illustrate the approach with the case of intelligent control in a medical application. Simulation results for this application show that we are able to find an optimal set of rules and membership functions for the fuzzy control system.

Keywords

control system synthesis; fuzzy control; fuzzy set theory; fuzzy systems; genetic algorithms; intelligent control; medical control systems; evolutionary approach; fuzzy control system design; fuzzy logic; fuzzy rules; fuzzy system optimization; genetic algorithms; intelligent control; medical control systems; membership functions; Anesthesia; Automatic control; Computer science; Design optimization; Fuzzy control; Fuzzy logic; Fuzzy systems; Genetic algorithms; Intelligent control; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the

Print_ISBN

0-7803-8376-1

Type

conf

DOI

10.1109/NAFIPS.2004.1336294

Filename

1336294