Recurrent fuzzy controller design by two-stage genetic algorithm with local and global mapping searches

Author

Juang, Chia-Feng

Author_Institution

Dept. of Electr. Eng., Nat. Chung-Hsing Univ., Taichung, Taiwan

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1228

Lastpage

1233

Abstract

A recurrent fuzzy controller design in reinforced learning environment using two stage genetic algorithm (TSGA) with concurrent local and global mapping searches is proposed. For the local mapping search stage, it searches through the well performed local rules. In this stage, each individual represents only a fuzzy rule. As to the global-mapping search stage each individual represents a whole fuzzy network. The stage determines which local rules should be combined together to achieve a good fuzzy network. Finally, the performance of TSGA is verified from simulation comparisons

Keywords

control system synthesis; fuzzy control; genetic algorithms; learning (artificial intelligence); neurocontrollers; recurrent neural nets; search problems; fuzzy network; global mapping searches; local mapping searches; recurrent fuzzy controller design; reinforced learning environment; two-stage genetic algorithm; Algorithm design and analysis; Automatic control; Design methodology; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Genetic algorithms; Neural networks; Symbiosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7280-8

Type

conf

DOI

10.1109/FUZZ.2002.1006679

Filename

1006679