Abstract
This paper proposed a novel self-adaptive wavelet network model for Regression Analysis. The structure of this network is distinguished from those of the present models. It has four layers. This model not only can overcome the structural redundancy which the present wavelet network cannot do, but also can solve the complicated problems respectively. Thus, generalization performance has been greatly improved; moreover, rapid learning can be realized. Some experiments on regression analysis are presented for illustration. Compared with the existing results, the model reaches a hundredfold improvement in speed and its generalization performance has been greatly improved.
This work was supported by the National Science Foundation of China (Grant No.60375021) and the Science Foundation of Hunan Province (Grant No.00JJY3096) and the Key Project of Hunan Provincial Education Department (Grant No.04A056).
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Gao, X., Zhang, J. (2005). A Novel Orthonormal Wavelet Network for Function Learning. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_44
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DOI: https://doi.org/10.1007/11539087_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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