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Multi Groups Cooperation based Symbiotic Evolution for TSK-type Neuro-Fuzzy Systems Design
Yi-Chang Cheng1, Yung-Chi Hsu, Sheng-Fuu Lin
1Department of Electrical and Control Engineering, National Chiao-Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.
Abstract:
In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations.
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