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Nonlinear System Identification Using Multi-resolution Reproducing Kernel Based Support Vector Regression

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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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.

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Author information

Authors and Affiliations

  1. School of Mathematics & Computer Science, School of Electric Information, Xihua University, Chengdu, Sichuan, 610039, China

    Hong Peng, Jun Wang, Min Tang & Lichun Wan

Authors
  1. Hong Peng
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  2. Jun Wang
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  3. Min Tang
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  4. Lichun Wan
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Peng, H., Wang, J., Tang, M., Wan, L. (2006). Nonlinear System Identification Using Multi-resolution Reproducing Kernel Based Support Vector Regression. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_117

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  • DOI: https://doi.org/10.1007/11760023_117

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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