Abstract
This paper proposes an orthogonal least square algorithm based on QR decomposition (QR-OLS) for the neurons selection of the hidden layer of wavelet networks. This new algorithm divides the original neurons matrix into several parts to avoid comparing among the poor ones and uses QR decomposition to select the significant ones. It can avoid lots of meaningless calculation. This algorithm is applied to the wavelet network with the analysis of variance (ANOVA) expansion and one-step-ahead predictions, respectively, for the Mackey-Glass delay-differential equation and the annual sunspot data set. The results show that the QR-OLS algorithm can relieve the load of the heave calculation and has a good performance.
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Han, M., Yin, J. (2007). Orthogonal Least Squares Based on QR Decomposition for Wavelet Networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_68
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DOI: https://doi.org/10.1007/978-3-540-72393-6_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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