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A Sparse Infrastructure of Wavelet Network for Nonparametric Regression
- Authors
- Zhang, Jun; Gu, Zhenghui; Li, Yuanqing; Gao, Xieping
- Issue Date
- Jun-2010
- Publisher
- Springer Verlag
- Keywords
- Wavelet network; neural network; sparse infrastructure; Regression
- Citation
- Advances in Neural Networks -- ISNN 2010 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I, v.6063, pp 347 - 354
- Pages
- 8
- Indexed
- SCIE
SCOPUS
- Journal Title
- Advances in Neural Networks -- ISNN 2010 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I
- Volume
- 6063
- Start Page
- 347
- End Page
- 354
- 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 Bunny's real-time neural network shows that, at similar speed, our model achieves improvement in generalization performance. abstract environment.
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Related Researcher
- ZHANG, Jun
- ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)