A fuzzy neural network system based on generalized class cover problem

Authors:
Yanxin Huang;Yan Wang;Wengang Zhou;Chunguang Zhou
Affiliations:
College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun, China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun, China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun, China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun, China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

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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.