A sparse infrastructure of wavelet network for nonparametric regression

Authors:
Jun Zhang;Zhenghui Gu;Yuanqing Li;Xieping Gao
Affiliations:
Center for Brain-Computer Interface and Brain Information Processing, South China University of Technology, Guangzhou, China;Center for Brain-Computer Interface and Brain Information Processing, South China University of Technology, Guangzhou, China;Center for Brain-Computer Interface and Brain Information Processing, South China University of Technology, Guangzhou, China;Information Engineering College, Xiangtan University, Xiangtan, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

Citing 2
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Wavelet neural networks for function learning

IEEE Transactions on Signal Processing
Real-time learning capability of neural networks

IEEE Transactions on Neural Networks

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Abstract

In this paper, we propose a novel 4-layer infrastructure of wavelet network It differs from the commonly used 3-layer wavelet networks in adaptive selection of wavelet neurons based on the input information As a result, it not only alleviates widespread structural redundancy, but can also control the scale of problem solution to a certain extent Based on this architecture, we build a new type of wavelet network for function learning The experimental results demonstrate that our model is remarkably superior to two well-established 3-layer wavelet networks in terms of both speed and accuracy Another comparison to Huang's real-time neural network shows that, at similar speed, our model achieves improvement in generalization performance abstract environment.