A fuzzy neural network system based on generalized class cover problem

Abstract

A voting-mechanism-based fuzzy neural network system based on generalized class cover problem and particle swarm optimization is proposed in this paper. When constructing the network structure, a generalized class cover problem and an improved greedy algorithm are adopted to get the class covers with relatively even radii, which are used to partition fuzzy input space and extract fewer robust fuzzy IF-THEN rules. Meanwhile, a weighted Mamdani inference mechanism is proposed to improve the efficiency of the system output and a particle swarm optimization-based algorithm is used to refine the system parameters. Experimental results show that the system is feasible and effective. © Springer-Verlag Berlin Heidelberg 2005.