GLOBAL OPTIMIZATION OF RADIAL BASIS FUNCTION NETWORKS BY HYBRID SIMULATED ANNEALING

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This paper presents a global optimization method of radial basis function networks. In the proposed method, stochastic search by simulated annealing is combined with a local search technique in order to perform global optimization of the network parameters with enhanced convergence speed. Its convergence property is proved mathematically. Experimental results demonstrate that the proposed method improves the performance of the networks over the conventional local and global training methods and reduces influence of the initial parameter values on the final results.
Publisher
ACAD SCIENCES CZECH REPUBLIC
Issue Date
2010
Language
English
Article Type
Article
Keywords

BASIS FUNCTION CENTERS; NEURAL-NETWORKS; LEARNING ALGORITHM; APPROXIMATION; PREDICTION

Citation

NEURAL NETWORK WORLD, v.20, no.4, pp.519 - 537

ISSN
1210-0552
URI
http://hdl.handle.net/10203/95041
Appears in Collection
EE-Journal Papers(저널논문)
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