Reinforcement group cooperation-based symbiotic evolution for recurrent wavelet-based neuro-fuzzy systems
This paper proposes a recurrent wavelet-based neuro-fuzzy system (RWNFS) with a reinforcement group cooperation-based symbiotic evolution (R-GCSE) for solving various control problems. The R-GCSE is different from the traditional symbiotic evolution. In the R-GCSE method, a population is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperates with other groups to generate better chromosomes by using the proposed elite-based compensation crossover strategy (ECCS). In this paper, the proposed R-GCSE is used to evaluate numerical control problems. The performance of the R-GCSE in the simulations is excellent compared with other existing models.
symbiotic evolution neuro-fuzzy system control reinforcement learning recurrent network