A fuzzy neural network system based on generalized class cover and particle swarm optimization

  • Authors:

  • Yanxin Huang;Yan Wang;Wengang Zhou;Zhezhou Yu;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;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol, Computation and Knowledge Engineering of the National Education Ministry, Changchun, China

  • Venue:
  • ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
  • Year:
  • 2005

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Abstract

A voting-mechanism-based fuzzy neural network system is proposed in this paper. When constructing the network structure, a generalized class cover problem is presented and its two solving algorithm, an improved greedy algorithm and a binary particle swarm optimization algorithm, are proposed 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 adopted to improve the efficiency of the system output and a real-valued particle swarm optimization-based algorithm is used to refine the system parameters. Experimental results show that the system is feasible and effective.