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Neural Computing and Applications

Erschienen in:

01.06.2009 | ISNN 2008

A gradient-based sequential radial basis function neural network modeling method

verfasst von: Wen Yao, Xiaoqian Chen, Wencai Luo

Erschienen in: Neural Computing and Applications | Ausgabe 5/2009

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Abstract

Radial basis function neural network (RBFNN) is widely used in nonlinear function approximation. One of the key issues in RBFNN modeling is to improve the approximation ability with samples as few as possible, so as to limit the network’s complexity. To solve this problem, a gradient-based sequential RBFNN modeling method is proposed. This method can utilize the gradient information of the present model to expand the sample set and refine the model sequentially, so as to improve the approximation accuracy effectively. Two mathematical examples and one practical problem are tested to verify the efficiency of this method.

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Titel: A gradient-based sequential radial basis function neural network modeling method
verfasst von: Wen Yao
Xiaoqian Chen
Wencai Luo
Publikationsdatum: 01.06.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 5/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0249-z

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