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.
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© 2007 Springer-Verlag Berlin Heidelberg
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Cheng, Y., Wang, X., Sun, W. (2007). A Study of Constructive Fuzzy Normalized RBF Neural Networks. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_1
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DOI: https://doi.org/10.1007/978-3-540-74282-1_1
Publisher Name: Springer, Berlin, Heidelberg
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