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Stability of Stochastic Neutral Cellular Neural Networks

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Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4682))

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Abstract

In this paper, we study a class of stochastic neutral cellular neural networks. By constructing a suitable Lyapunov functional and employing the nonnegative semi-martingale convergence theorem we give some sufficient conditions ensuring the almost sure exponential stability of the networks. The results obtained are helpful to design stability of networks when stochastic noise is taken into consideration. Finally, two examples are provided to show the correctness of our analysis.

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Author information

Authors and Affiliations

  1. Basic Department, Jinling Institute of Technology, Nanjing 210001, China

    Ling Chen

  2. Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

    Ling Chen & Hongyong Zhao

Authors
  1. Ling Chen
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  2. Hongyong Zhao
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Editor information

De-Shuang Huang Laurent Heutte Marco Loog

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© 2007 Springer-Verlag Berlin Heidelberg

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Chen, L., Zhao, H. (2007). Stability of Stochastic Neutral Cellular Neural Networks. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_17

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  • DOI: https://doi.org/10.1007/978-3-540-74205-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74201-2

  • Online ISBN: 978-3-540-74205-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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