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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 198–203Cite as

LMI Approach to Robust Stability Analysis of Cohen-Grossberg Neural Networks with Multiple Delays

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

Abstract

Robust stability analysis of a class of Cohen-Grossberg neural networks with multiple delays is given. An approach combining the Lyapunov functional with the linear matrix inequality (LMI) is taken to obtain the sufficient condition for the globally robust stability of equilibrium point. The result is established without assuming the differentiability and monotonicity of activation functions.

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References

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Author information

Authors and Affiliations

  1. Institute of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110004, P.R. China

    Ce Ji, Hua-Guang Zhang & Chong-Hui Song

Authors
  1. Ce Ji
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  2. Hua-Guang Zhang
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  3. Chong-Hui Song
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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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Cite this paper

Ji, C., Zhang, HG., Song, CH. (2006). LMI Approach to Robust Stability Analysis of Cohen-Grossberg Neural Networks with Multiple Delays. 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_30

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  • DOI: https://doi.org/10.1007/11759966_30

  • 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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