Design of interval type-2 FCM-based FNN and genetic optimization for pattern recognition

Abstract

A new category of fuzzy neural networks with multiple outputs based on an interval type-2 fuzzy c-means clustering algorithm (IT2FCM-based FNNm) for pattern recognition is proposed in this paper. The premise part of the rules of the proposed network is realized with the aid of the scatter partition of the input space generated by the IT2FCM clustering algorithm. The number of the partition of input space equals the number of clusters, and the individual partitioned spaces describe the fuzzy rules. The consequence part of the rules is represented by polynomial functions with an interval set along with multiple outputs. The coefficients of the polynomial functions are learned by the back-propagation (BP) algorithm. To optimize the parameters of the IT2FCM-based FNNm, we consider real-coded genetic algorithms. The proposed network is evaluated with the use of numerical experimentation for pattern recognition. © 2012 Springer-Verlag.