Online Least Squares Support Vector Machines Based on Wavelet and Its Applications

Authors:
Qian Zhang;Fuling Fan;Lan Wang
Affiliations:
School of Electronic Information, ZhongYuan Institute of Technology, Zhengzhou 450007, China;School of Electronic Information, ZhongYuan Institute of Technology, Zhengzhou 450007, China;School of Electronic Information, ZhongYuan Institute of Technology, Zhengzhou 450007, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

As the conventional training algorithms of least squares support vector machines (LS-SVM) are inefficient in online applications, an online learning algorithm is proposed. The online algorithm is suitable for the large data set and practical applications where the data come in sequentially. Aiming at the characteristics of signals, a wavelet kernel satisfying wavelet frames is presented. The wavelet kernel can approximate arbitrary functions in quadratic continuous integral space, hence the generalization ability of LS-SVM is improved. To illustrate its favorable performance, the wavelet based online LS-SVM (WOLS-SVM) is applied to nonlinear system identification. The simulation results show that the WOLS-SVM outperforms the existing algorithms with higher learning efficiency as well as better accuracy, and indicate its effectiveness.