Performance evaluation of two radial basis function neural network models

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Performance evaluation of two radial basis function neural network models

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dc.contributor.author Chistova, Nikolinka G
dc.contributor.author Vachkov, Gancho L
dc.date.accessioned 2017-02-07T12:58:10Z
dc.date.available 2017-02-07T12:58:10Z
dc.date.issued 2014
dc.identifier.issn 978-1-61804-244-6
dc.identifier.uri http://hdl.handle.net/123456789/1387
dc.description.abstract In this paper performance evaluation of two modification of the classical Radial Basis Function Neural Network (RBFNN) model, called Reduced and Simplified RBFNN models is carried out. Different RBFNN models with different number of Radial Basis Functions (RBFs) are created and analyzed. Particle Swarm Optimization (PSO) algorithm with constraints for the parameter tuning of the models is applied. Simultaneous optimization of all three groups of parameters, namely the centers, widths and the weights of the RBFNN is performed. It is shown that the Simplified RBFNN models, which have smaller number of parameters, can achieve even better modeling accuracy than the Reduced RBFNN models. en_US
dc.language.iso en en_US
dc.relation.ispartofseries Latest Trends on Systems - Volume II;
dc.subject particle swarm optimization en_US
dc.subject performance evaluation en_US
dc.subject radial basis function neural networks en_US
dc.title Performance evaluation of two radial basis function neural network models en_US
dc.type Article en_US


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