A New Fast Algorithm for Fuzzy Rule Selection

This paper investigates the selection of fuzzy rules for fuzzy neural networks. The main objective is to effectively and efficiently select the rules and to optimize the associated parameters simultaneously. This is achieved by the proposal of a fast forward rule selection algorithm (FRSA), where the rules are selected one by one and a residual matrix is recursively updated in calculating the contribution of rules. Simulation results show that, the proposed algorithm can achieve faster selection of fuzzy rules in comparison with conventional orthogonal least squares algorithm, and better network performance than the widely used error reduction ratio method (ERR).