Optimal Design of Fuzzy Clustering-Based Fuzzy Neural Networks for Pattern Classification
The authors introduce a new category of fuzzy neural networks with multiple-output based on fuzzy clustering algorithm, especially, Fuzzy C-Means clustering algorithm (FCM-based FNNm) for pattern classification in this paper. The premise part of the rules of the proposed networks is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The partitioned local spaces describe the fuzzy rules and the number of the partitioned local spaces is equal to the number of clusters. Due to these characteristics, they may alleviate the problem of the curse of dimensionality. The consequence part of the rules is represented by polynomial functions with multiple-output for pattern classification. And the coefficients of the polynomial functions are learned by back propagation algorithm.