Solving a system of nonlinear integral equations by an RBF network

Authors:
A. Golbabai;M. Mammadov;S. Seifollahi
Affiliations:
Department of Mathematics, Iran University of Science and Technology, Narmak, Tehran 16844, Iran;School of Information Technology and Mathematical Science, Ballarat University, Ballarat VIC 3350, Australia;Department of Mathematics, Iran University of Science and Technology, Narmak, Tehran 16844, Iran
Venue:
Computers & Mathematics with Applications
Year:
2009

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Abstract

In this paper, a novel learning strategy for radial basis function networks (RBFN) is proposed. By adjusting the parameters of the hidden layer, including the RBF centers and widths, the weights of the output layer are adapted by local optimization methods. A new local optimization algorithm based on a combination of the gradient and Newton methods is introduced. The efficiency of some local optimization methods to update the weights of RBFN is studied in solving systems of nonlinear integral equations.