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Robust Stability Analysis of Uncertain Hopfield Neural Networks with Markov Switching

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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

The robust stability of uncertain Hopfield neural networks with Markov switching is analyzed, the parametric uncertainty is assumed to be norm bounded. Sufficient conditions for the exponential stability are established by constructing suitable Lyapunov functionals. The stability criteria represented in terms of linear matrix inequalities (LMIs), and are computationally efficient.

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Author information

Authors and Affiliations

  1. School of Information Engineering, China University of Geosciences, Beijing, 100083, China

    Bingji Xu & Qun Wang

Authors
  1. Bingji Xu
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  2. Qun Wang
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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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Xu, B., Wang, Q. (2006). Robust Stability Analysis of Uncertain Hopfield Neural Networks with Markov Switching. 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_39

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  • DOI: https://doi.org/10.1007/11759966_39

  • 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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