Chapter

Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques

Volume 2 of the series Communications in Computer and Information Science pp 1-10

A Study of Constructive Fuzzy Normalized RBF Neural Networks

  • Yuhu ChengAffiliated withSchool of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221008
  • , Xuesong WangAffiliated withSchool of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221008
  • , Wei SunAffiliated withSchool of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221008

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Abstract

One of the difficulties encountered in the application of fuzzy radial basis function (RBF) neural network is how to determine the number of hidden neurons, which is also called network structure learning problem. In order to solve the above problem and to improve the generalization performance of fuzzy RBF network, a kind of constructive T-S fuzzy normalized RBF network was proposed by combining T-S fuzzy model with normalized RBF network. The meaning of ’constructive’ is that the hidden neurons can be added, merged and deleted dynamically according to the task complexity and the learning progress without any prior knowledge. Simulation results of nonlinear function approximation show that the proposed fuzzy normalized RBF network has perfect approximation property with economical network size.

Keywords

Constructive neural network Normalized RBF neural network Network structure learning Network size