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Robust Stability of Stochastic Neural Networks with Interval Discrete and Distributed Delays

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Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5863))

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Abstract

This paper is concerned with the global asymptotic stability analysis problem for a class of stochastic neural networks with interval discrete and distributed delays. The parameter uncertainties are assumed to be norm bounded. Based on Lyapunov-Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality(LMI) approach. It is also shown that the result in this paper cover some recently published works. A numerical example is provided to demonstrate the usefulness of the proposed criteria.

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Author information

Authors and Affiliations

  1. Department of Control Science and engineering, Huazhong University of Science and Technology, 430074, Wuhan, China

    Song Zhu, Yi Shen & Guici Chen

Authors
  1. Song Zhu
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  2. Yi Shen
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  3. Guici Chen
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

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© 2009 Springer-Verlag Berlin Heidelberg

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Zhu, S., Shen, Y., Chen, G. (2009). Robust Stability of Stochastic Neural Networks with Interval Discrete and Distributed Delays. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_29

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  • DOI: https://doi.org/10.1007/978-3-642-10677-4_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10676-7

  • Online ISBN: 978-3-642-10677-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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