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International Symposium on Neural Networks

ISNN 2010: Advances in Neural Networks - ISNN 2010 pp 585–594Cite as

The Existence of Anti-periodic Solutions for High-Order Cohen-Grossberg Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6063))

Abstract

In this paper, we use the Lyapunov function to establish new results on the existence uniqueness and globally exponential stablity of anti-periodic solutions for high-order Cohen-Grossberg neural networks with time-varying delays. Finally, an example and its simulation are given to illustrate the feasibility and effectiveness of our results.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Yuxi Normal University, Yuxi, Yunnan, 653100, China

    Zhouhong Li, Kaihong Zhao & Chenxi Yang

Authors
  1. Zhouhong Li
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  2. Kaihong Zhao
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  3. Chenxi Yang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu

  3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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© 2010 Springer-Verlag Berlin Heidelberg

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Li, Z., Zhao, K., Yang, C. (2010). The Existence of Anti-periodic Solutions for High-Order Cohen-Grossberg Neural Networks. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_75

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  • DOI: https://doi.org/10.1007/978-3-642-13278-0_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13277-3

  • Online ISBN: 978-3-642-13278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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