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DETERMINING THE STRUCTURES AND PARAMETERS OF RADIAL BASIS FUNCTION NEURAL NETWORKS USING IMPROVED GENETIC ALGORITHMS 被引量:1

DETERMINING THE STRUCTURES AND PARAMETERS OF RADIAL BASIS FUNCTION NEURAL NETWORKS USING IMPROVED GENETIC ALGORITHMS
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摘要 Powelproposedradialbasisfunction(RBF)methodforstrictmultivariablefunctioninterpola-tion[1].BroomheadandLowefirstlyappliedRBFw... The method of determining the structures and parameters of radial basis function neural networks(RBFNNs) using improved genetic algorithms is proposed. Akaike′s information criterion (AIC) with generalization error term is used as the best criterion of optimizing the structures and parameters of networks. It is shown from the simulation results that the method not only improves the approximation and generalization capability of RBFNNs ,but also obtain the optimal or suboptimal structures of networks.
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出处 《Journal of Central South University》 SCIE EI CAS 1998年第2期68-73,共6页 中南大学学报(英文版)
关键词 RADIAL BASIS function neural network GENETIC algorithms Akaike′s information CRITERION OVERFITTING radial basis function neural network genetic algorithms Akaike′s information criterion overfitting
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