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Existence and Stability of Periodic Solution in a Class of Impulsive Neural Networks

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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

In this paper, we initiate the study of a class of neural networks with impulses. A sufficient condition for the existence and global exponential stability of a unique periodic solution of the networks is established. Our condition does not assume the differentiability or monotonicity of the activation functions.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chongqing University, Chongqing, 400044, China

    Xiaofan Yang & Yuanyan Tang

  2. Parallelism, Algorithms and Architectures Research Centre, Department of Computer Science, Loughborough University, Loughborough, Leicestershire, LE11 3TU, UK

    David J. Evans

  3. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Yuanyan Tang

Authors
  1. Xiaofan Yang
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  2. David J. Evans
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  3. Yuanyan Tang
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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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Yang, X., Evans, D.J., Tang, Y. (2005). Existence and Stability of Periodic Solution in a Class of Impulsive Neural Networks. 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_41

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  • DOI: https://doi.org/10.1007/11427391_41

  • 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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