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
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;
Conference_Titel :
Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on
Print_ISBN :
0-7803-7810-5
DOI :
10.1109/FUZZ.2003.1209368
