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Globally Stable Periodic State of Delayed Cohen-Grossberg 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 have obtained some sufficient conditions to guarantee that Cohen-Grossberg neural networks with discrete and distributed delays have a periodic orbit and this periodic orbit are globally attractive. The results presented in this paper are the improvement and extension of the existed ones in some existing works. Finally, the validity and performance of the results are illustrated by two simulation examples.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Hubei Normal University, Huangshi, Hubei, 435002, China

    Chaojin Fu

  2. Department of Basic Courses, Naval University of Engineering, Hubei, Wuhan, 430033, China

    Hanlin He

  3. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China

    Xiaoxin Liao

Authors
  1. Chaojin Fu
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  2. Hanlin He
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  3. Xiaoxin Liao
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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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Fu, C., He, H., Liao, X. (2005). Globally Stable Periodic State of Delayed Cohen-Grossberg 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_43

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  • DOI: https://doi.org/10.1007/11427391_43

  • 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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