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Global Exponential Stability of Reaction-Diffusion Hopfield Neural Networks with Distributed Delays

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

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Abstract

The global exponential stability of reaction-diffusion Hopfield neural networks with distributed delays is studied. Without assuming the boundedness, monotonicity and differentiability of the activation functions, the sufficient conditions were obtained by utilizing Dini’s derivative, F-function and extended Hanaly’s inequality. These conditions are easy to check and apply in practice and can be regarded as an extension of existing results.

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Author information

Authors and Affiliations

  1. College of Automation Science and Engineering, South China University of Technology, Guangdong, Guangzhou, 510640, China

    Zhihong Tang, Yiping Luo & Feiqi Deng

  2. Hunan Institute of Engineering, Xiangtan, Hunan, 411101, China

    Yiping Luo

Authors
  1. Zhihong Tang
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  2. Yiping Luo
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  3. Feiqi Deng
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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. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Tang, Z., Luo, Y., Deng, F. (2005). Global Exponential Stability of Reaction-Diffusion Hopfield Neural Networks with Distributed Delays. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_26

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  • DOI: https://doi.org/10.1007/11427391_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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