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Nonlinear Blind Source Separation Using Hybrid Neural Networks

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

This paper proposes a novel algorithm based on minimizing mutual information for a special case of nonlinear blind source separation: post-nonlinear blind source separation. A network composed of a set of radial basis function (RBF) networks, a set of multilayer perceptron and a linear network is used as a demixing system to separate sources in post-nonlinear mixtures. The experimental results show that our proposed method is effective, and they also show that the local character of the RBF network’s units allows a significant speedup in the training of the system.

This work was supported by the National Science Foundation of China (Nos.60472111, 30570368 and 60405002).

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Author information

Authors and Affiliations

  1. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, P.O.Box 1130, Hefei, Anhui, China

    Chun-Hou Zheng & Zhi-Kai Huang

  2. Department of Automation, University of Science and Technology of China,  

    Chun-Hou Zheng & Zhi-Kai Huang

  3. Computer Science & Engineering Dept., The Chinese University of Hong Kong, Hong Kong

    Michael R. Lyu

  4. Information Engineering Dept., The Chinese University of Hong Kong, Shatin, Hong Kong

    Tat-Ming Lok

Authors
  1. Chun-Hou Zheng
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  2. Zhi-Kai Huang
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  3. Michael R. Lyu
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  4. Tat-Ming Lok
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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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Zheng, CH., Huang, ZK., Lyu, M.R., Lok, TM. (2006). Nonlinear Blind Source Separation Using Hybrid Neural Networks. 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 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_172

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  • DOI: https://doi.org/10.1007/11759966_172

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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