2012 ©
             Publication
Journal Publication
Research Title Evolutionary training of a q-Gaussian radial basis functional-link nets for function approximation 
Date of Distribution 25 July 2013 
Conference
     Title of the Conference 10th International Joint Conference on Computer Science and Software Engineering (JCSSE) 
     Organiser Department of Computer Science, Faculty of Informatics, Mahasakham University 
     Conference Place Pullman Khon Kaen Raja Orchid hotel 
     Province/State Khonkaen, Thailand 
     Conference Date 29 May 2013 
     To 31 May 2013 
Proceeding Paper
     Volume
     Issue
     Page 58-63 
     Editors/edition/publisher  
     Abstract In this paper, radial basis functional-link nets (RBFLNs) based on a q-Gaussian function is proposed. In order to enhance the generalization performance of a modified radial basis function neural network and enhance the performance of the new network, the evolutionary algorithm named real-coded chemical reaction optimization (RCCRO), is presented for training the new network. A developed RCCRO, has been shown to perform well in many optimization problems. A RCCRO is employed to select the non-extensive entropic index q and the other parameters of the network. The experimental results of the function approximation show that the proposed approach can improve the performance of RBFLNs. 
Author
547020034-9 Mr. NIPOTEPAT MUANGKOTE [Main Author]
Science Doctoral Degree

Peer Review Status มีผู้ประเมินอิสระ 
Level of Conference นานาชาติ 
Type of Proceeding Full paper 
Type of Presentation Oral 
Part of thesis false 
Presentation awarding false 
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