Title :
Group-based Evolutionary Swarm Intelligence for Recurrent Fuzzy Controller Design
Author :
Juang, Chia-Feng ; Chung, I-Fang ; Chen, Shin-Kuan
Author_Institution :
Nat. Chung Hsing Univ., Taichung
Abstract :
Recurrent fuzzy controller design by the hybrid of multi-group genetic algorithm and particle swarm optimization (R-MGAPSO), is proposed in this paper. The recurrent fuzzy controller designed here is the Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN). Both the number of fuzzy rules and parameters in TRFN are designed concurrently by R-MGAPSO. Evolution of population consists of three major operations: group enhancement by particle swarm optimization, variable-length individual crossover and mutation. To verify the performance of R-MGAPSO, control of a dynamic plant is simulated and compared with other genetic algorithms.
Keywords :
control system synthesis; fuzzy control; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); neurocontrollers; particle swarm optimisation; recurrent neural nets; Takagi-Sugeno-Kang type network; fuzzy rule; group-based evolutionary swarm intelligence; multigroup genetic algorithm; particle swarm optimization; recurrent fuzzy controller design; Algorithm design and analysis; Feedback loop; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Genetic mutations; Learning systems; Particle swarm optimization; Takagi-Sugeno-Kang model;
Conference_Titel :
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9488-7
DOI :
10.1109/FUZZY.2006.1681936
