Design of FCM-Based Fuzzy Neural Networks and Its Optimization for Pattern Recognition

Abstract

In this paper, we introduce a new category of fuzzy neural network with multi-output based on fuzzy c-means clustering algorithm (FCM-based FNNm). The premise part of the rules of the proposed network is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with multi-output. And the coefficients of the polynomial functions are learned by BP algorithm. To optimize the parameters of FCM-based FNNm we consider real-coded genetic algorithms. The proposed network is evaluated with the use of numerical experimentation.