Wavelets based neural network for function approximation

Authors:
Yong Fang;Tommy W. S. Chow
Affiliations:
School of Communication and Information Engineering, Shanghai University, Shanghai, China;Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 2
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Abstract

In this paper, a new type of WNN is proposed to enhance the function approximation capability. In the proposed WNN, the nonlinear activation function is a linear combination of wavelets, that can be updated during the networks training process. As a result the approximate error is significantly decreased. The BP algorithm and the QR decomposition based training method for the proposed WNN is derived. The obtained results indicate that this new type of WNN exhibits excellent learning ability compared to the conventional ones.