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Erschienen in:
2005 | OriginalPaper | Buchkapitel
A Fast Nonseparable Wavelet Neural Network for Function Approximation
verfasst von : Jun Zhang, Xieping Gao, Chunhong Cao, Fen Xiao
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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In this paper, based on the theory of nonseparable wavelet, a novel nonseparable wavelet model has been proposed. The structure of the model is distinguished from that of wavelet network (RBF structure). It is a four-layer structure, which helps overcome the structural redundancy. In the process of the training of the network, in the light of the characteristics of nonseparable wavelet, a novel method of setting the initial value of weight has been proposed. It can overcome the shortcoming of gradient descent methodology that it makes the convergence of the network slow. Some experiments with the novel model for function learning will be shown. Comparing with the present wavelet networks, BP network, the results in this paper show that the speed and generalization performance of the novel model have been greatly improved.