A Global Optimization Method of Radial Basis Function Networks for Function Approximation 


Vol. 14,  No. 5, pp. 377-382, Oct.  2007
10.3745/KIPSTB.2007.14.5.377


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  Abstract

This paper proposes a training algorithm for global optimization of the parameters of radial basis function networks. Since conventional training algorithms usually perform only local optimization, the performance of the network is limited and the final network significantly depends on the initial network parameters. The proposed hybrid simulated annealing algorithm performs global optimization of the network parameters by combining global search capability of simulated annealing and local optimization capability of gradient-based algorithms. Via experiments for function approximation problems, we demonstrate that the proposed algorithm can find networks showing better training and test performance and reduce effects of the initial network parameters on the final results.

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[IEEE Style]

J. S. Lee and C. H. Park, "A Global Optimization Method of Radial Basis Function Networks for Function Approximation," The KIPS Transactions:PartB , vol. 14, no. 5, pp. 377-382, 2007. DOI: 10.3745/KIPSTB.2007.14.5.377.

[ACM Style]

Jong Seok Lee and Cheol Hoon Park. 2007. A Global Optimization Method of Radial Basis Function Networks for Function Approximation. The KIPS Transactions:PartB , 14, 5, (2007), 377-382. DOI: 10.3745/KIPSTB.2007.14.5.377.

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