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Global Asymptotic Stability of Cohen-Grossberg Neural Networks with Multiple Discrete Delays

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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

The asymptotic stability is analyzed for Cohen-Grossberg neural networks with multiple discrete delays. The boundedness, differentiability or monotonicity condition is not assumed on the activation functions. The generalized Dahlquist constant approach is employed to examine the existence and uniqueness of equilibrium of the neural networks, and a novel Lyapunov functional is constructed to investigate the stability of the delayed neural networks. New general sufficient conditions are derived for the global asymptotic stability of the neural networks with multiple delays.

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Author information

Authors and Affiliations

  1. School of Mathematics and Computational Science, Sun Yat-Sen University, 510275 Guangzhou, China

    Anhua Wan

  2. Institute of Automation, Chinese Academy of Sciences, 100080 Beijing, China

    Anhua Wan & Hong Qiao

  3. Department of Applied Mathematics, College of Science, South China Agricultural University, 510642 Guangzhou, China

    Weihua Mao

  4. College of Automation Science and Engineering, South China University of Technology, 510641 Guangzhou, China

    Weihua Mao

  5. Institute of Applied Mathematics, Chinese Academy of Sciences, 100080 Beijing, China

    Bo Zhang

Authors
  1. Anhua Wan
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  2. Weihua Mao
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  3. Hong Qiao
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  4. Bo Zhang
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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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Wan, A., Mao, W., Qiao, H., Zhang, B. (2007). Global Asymptotic Stability of Cohen-Grossberg Neural Networks with Multiple Discrete Delays. 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_6

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  • DOI: https://doi.org/10.1007/978-3-540-74205-0_6

  • 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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