NDLI: Wavelet neural networks employing over-complete number of compactly supported non-orthogonal wavelets and their applications

Content Provider	IEEE Xplore Digital Library
Author	Yamakawa, T. Uchino, E. Samatsu, T.
Copyright Year	1994
Description	Author affiliation: Kyushu Inst. of Technol., Fukuoka, Japan (Yamakawa, T.; Uchino, E.; Samatsu, T.)
Abstract	This paper proposes two types of new neuron models, WS neuron (wavelet synapse neuron) and WA neuron (wavelet activation function neuron), which are obtained by modifying a traditional neuron model with non-orthogonal wavelet bases, while Boubez et al. (1993) employed orthonormal wavelets. Four types of typical wavelet neural networks employing WS and/or WA neurons are discussed. The simplest wavelet neural network exhibits much higher ability of generalization and much shorter time for learning rather than a three-layered feedforward neural network. Furthermore the wavelet neural network is guaranteed to give the global minimum. Other three wavelet neural networks are examined for predicting chaotic behaviour of a nonlinear dynamical system. The performance in learning speed and prediction of wavelet neural networks are more significant than a four-layered feedforward neural network.
Starting Page	1391
Ending Page	1396
File Size	484205
Page Count	6
File Format	PDF
ISBN	078031901X
DOI	10.1109/ICNN.1994.374489
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Feedforward neural networks Neurons Shape Nonlinear dynamical systems Feeds Computer networks System identification Art Chaos
Content Type	Text
Resource Type	Article

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