A Compensatory Fuzzy Neural Network Modeling Method Based on Clustering and Cultural Algorithms

According to the characteristic of compensatory fuzzy neural networks(CFNN)，this paper proposes a compensatory fuzzy neural network based on clustering and cultural algorithms. The identification of the proposed network is composed of two steps：structure identification and parameter identification．In the process of structure identification，the improved clustering method is used to obtain the number of inference rules of fuzzy model and the initial parameters in order to construct the initial fuzzy model; During parameter identification，cultural algorithm based on multilayer belief spaces is used to optimize the parameters of the compensatory fuzzy neural network with five layers so that the model obtains a higher accuracy. Finally, the result of the fault diagnosis modeling of TEP shows that the proposed network is superior to the conventional FNN and CFNN in modeling precise and convergence rate.