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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 185–191Cite as

Robust Periodicity in Recurrent Neural Network with Time Delays and Impulses

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

Abstract

In this paper, the robust periodicity for recurrent neural networks with time delays and impulses is investigated. Based on Lyapunov method and fixed point theorem, a sufficient condition of global exponential robust stability of periodic solution is obtained.

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Author information

Authors and Affiliations

  1. School of Science, Southern Yangtze University, Wuxi, 214122, China

    Yongqing Yang

  2. Department of Mathematics, Southeast University, Nanjing, 210096, China

    Yongqing Yang

Authors
  1. Yongqing Yang
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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. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Yang, Y. (2006). Robust Periodicity in Recurrent Neural Network with Time Delays and Impulses. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_28

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  • DOI: https://doi.org/10.1007/11759966_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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