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Global Exponential Synchronization of a Class of Chaotic Neural Networks with Time-Varying 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

This paper aims to present a synchronization scheme for a class of chaotic neural networks with time-varying delays, which covers the Hopfield neural networks and cellular neural networks. Using the drive-response concept, a control law of two identical chaotic neural networks is derived to achieve the exponential synchronization. Furthermore, based on the idea of vector Lyapunov function, and M-matrix theory, the sufficient conditions for global exponential synchronization of a class of chaotic neural networks are obtained. The synchronization condition is easy to verify and removed some restriction on the chaotic neural networks. Finally, some chaotic neural networks with time-varying delays are given as examples for illustration.

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Authors and Affiliations

  1. National Traction Power Laboratory, Southwest Jiaotong University, Chengdu 610031, China

    Jing Lin & Jiye Zhang

Authors
  1. Jing Lin
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  2. Jiye 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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Lin, J., Zhang, J. (2007). Global Exponential Synchronization of a Class of Chaotic Neural Networks with Time-Varying 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_9

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  • DOI: https://doi.org/10.1007/978-3-540-74205-0_9

  • 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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