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| An Improved GGAPRBF Algorithm and Its Application to Function Approximation |
| LI Bin1, LAI XiaoPing2 |
1.Department of Mathematical and Physical Sciences, Shandong Institute of Light Industry, Jinan 250353
2.School of Information Engineering, Shandong University at Weihai, Weihai 264209 |
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Abstract An improved learning algorithm for RBF neural network based on the GGAPRBF algorithm is proposed. The adaptive adjusting algorithm for the width of hidden neuron radial basis function and the dynamical regulation of overlap threshold are introduced into the GGAPRBF algorithm. The presented algorithm is compared with RAN, RANEKF, MRAN,IRAN and GGAPRBF(GAPRBF) algorithms by simulation on three benchmark problems in the function approximation area. The results indicate that the proposed algorithm provides good generalization performance with considerably reduction in network size and training time.
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Received: 14 March 2006
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2007, Vol. 20 
