Solving a system of nonlinear integral equations by an RBF network
journal contribution
posted on 2009-05-01, 00:00 authored by A Golbabai, Musa MammadovMusa Mammadov, S SeifollahiIn 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. © 2009 Elsevier Ltd. All rights reserved.
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Journal
Computers and Mathematics with ApplicationsVolume
57Pagination
1651-1658Location
Amsterdam, The NetherlandsPublisher DOI
Open access
- Yes
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0898-1221Language
engPublication classification
C1.1 Refereed article in a scholarly journalIssue
10Publisher
ElsevierUsage metrics
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