Nonlinear system identification using multi-resolution reproducing kernel based support vector regression

Authors:
Hong Peng;Jun Wang;Min Tang;Lichun Wan
Affiliations:
School of Mathematics & Computer Science, School of Electric Information, Xihua University, Chengdu, Sichuan, China;School of Mathematics & Computer Science, School of Electric Information, Xihua University, Chengdu, Sichuan, China;School of Mathematics & Computer Science, School of Electric Information, Xihua University, Chengdu, Sichuan, China;School of Mathematics & Computer Science, School of Electric Information, Xihua University, Chengdu, Sichuan, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

A new reproducing kernel in reproducing kernel Hilbert space (RKHS), namely the multi-resolution reproducing kernel, is presented in this paper. The multi-resolution reproducing kernel is generated by scaling basis function at some scale and wavelet basis function with different resolution. Based on multi-resolution reproducing kernel and ν- support vector regression (ν-SVR) method, a new regression model is proposed. The regression model used to nonlinear system identification, incorporate the advantage of the support vector machines and the multi-resolution property of wavelet. Simulation examples are given to illustrate the feasibility and effectiveness of the method.