DocumentCode
1603239
Title
Learning fuzzy model for nonlinear system using evolution strategies with adaptive direction mutation
Author
Yongquan, Yu ; Ying, Huang ; Bi, Zeng ; Xianchu, Chen
Author_Institution
Inst. of Intelligent Eng., Guangdong Univ. of Technol., Guangzhou, China
Volume
1
fYear
2003
Firstpage
237
Abstract
There are many methods to learn rules base for nonlinear system. The special method, the evolution strategies with adaptive direction, is presented in this paper. The actual steps and principle are also given. The simulation on results show this method is the effective one to learn rules base, and the faster generation rate can be obtained.
Keywords
fuzzy control; genetic algorithms; learning (artificial intelligence); nonlinear control systems; adaptive direction mutation; evolution strategies; faster generation rate; fuzzy control system; learning fuzzy model; linguistic model; nonlinear system; real number coding schema; rules base; self-adaptive method; Clustering algorithms; Fuzzy control; Fuzzy sets; Fuzzy systems; Genetic algorithms; Genetic mutations; Hybrid intelligent systems; Induction generators; Neural networks; Nonlinear systems;
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.2003.1209368
Filename
1209368
Link To Document