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.
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Zhang, J., Gu, Z., Li, Y., Gao, X. (2010). A Sparse Infrastructure of Wavelet Network for Nonparametric Regression. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_45
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DOI: https://doi.org/10.1007/978-3-642-13278-0_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13277-3
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