NDLI: Wavelet neural networks for function learning

Content Provider	IEEE Xplore Digital Library
Author	Jun Zhang Walter, G.G. Miao, Y. Wan Ngai Wayne Lee
Copyright Year	1991
Abstract	A wavelet-based neural network is described. The structure of this network is similar to that of the radial basis function (RBF) network, except that in the present paper the radial basis functions are replaced by orthonormal scaling functions that are not necessarily radial-symmetric. The efficacy of this type of network in function learning and estimation is demonstrated through theoretical analysis and experimental results. In particular, it has been shown that the wavelet network has universal and L/sup 2/ approximation properties and is a consistent function estimator. Convergence rates associated with these properties are obtained for certain function classes where the rates avoid the "curse of dimensionality". In the experiments, the wavelet network performed well and compared favorably to the MLP and RBF networks.<>
Sponsorship	IEEE Signal Processing Society
Starting Page	1485
Ending Page	1497
Page Count	13
File Size	1198208
File Format	PDF
ISSN	1053587X
Volume Number	43
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Radial basis function networks Convergence Learning Signal processing algorithms Manufacturing Predictive models Data analysis Signal processing Process control
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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