Orthogonal Least Squares Based on QR Decomposition for Wavelet Networks

Authors:
Min Han;Jia Yin
Affiliations:
School of Electronic and Information Engineering, Dalian University of Technology, Dalian, 116023, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, 116023, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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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.