A genetic-fuzzy-neuro model encodes FNNs using SWRM and BRM
Engineering Applications of Artificial Intelligence
Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
Optimization of fuzzy partitions for inductive reasoning using genetic algorithms
International Journal of Systems Science
IEEE Transactions on Fuzzy Systems
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing
Mathematical and Computer Modelling: An International Journal
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In this paper, an evolution-based approach to design of neural fuzzy networks is presented. The proposed strategy optimizes the whole fuzzy system with minimum rule number according to given specifications, while training the network parameters. The approach relies on an optimization tool, which combines evolution strategies and simulated annealing algorithms in finding the global optimum solution. The optimization variables include membership function parameters and rule numbers which are combined with genetic parameters to create diversity in the search space due to self-adaptation. The optimization technique is independent of the topology under consideration and capable of handling any type of membership function. The algorithmic details of the optimization methodology are discussed in detail, and the generality of the approach is illustrated by different examples