A HYBRID INTELLIGENT MODEL BASED ON EVOLUTIONARY FUZZY CLUSTERING AND SYNDICATE NEURAL NETWORKS

In this article, a new hybrid intelligent model comprising a cluster allocation and adaptation component is developed for solving classification and pattern recognition problems. Its computation ability has been verified through various benchmark problems and biometric applications. The proposed model consists of two components: cluster distribution and adaptation. In the first module, mean patterns are distributed into the number of clusters based on the evolutionary fuzzy clustering, which is the basis for network structure selection in next module. In the second module, training and subsequent generalization is performed by the syndicate neural networks (SNN). The number of SNNs required in the second module will be same as the number of clusters. Whereas each network contains as many output neurons as the maximum number of members assigned to each cluster. The proposed novel fusion of evolutionary fuzzy clustering with a neural network yields superior performance in classification and pattern recognition problems. Performance evaluation has been carried out over a wide spectrum of benchmark problems and real-life biometric recognition problems with noise and occlusion. Experimental results demonstrate the efficacy of the methodology over existing ones.