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Group-based Evolutionary Swarm Intelligence for Recurrent Fuzzy Controller Design | IEEE Conference Publication | IEEE Xplore

Group-based Evolutionary Swarm Intelligence for Recurrent Fuzzy Controller Design


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 fuzz...Show More

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.
Date of Conference: 16-21 July 2006
Date Added to IEEE Xplore: 11 September 2006
Print ISBN:0-7803-9488-7
Print ISSN: 1098-7584
Conference Location: Vancouver, BC, Canada

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