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Impulsive Robust Control of Interval Hopfield 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

This paper discusses impulsive control and synchronization of interval Hopfield neural networks (HNN for short). Based on the matrix measure and new comparison theorem, this paper presents an impulsive robust control scheme of the interval HNN. We derive some sufficient conditions for the stabilization and synchronization of interval Hopfield neural networks via impulsive control with varying impulsive intervals. Moreover, the large upper bound of impulsive intervals for the stabilization and synchronization of interval HNN can be obtained.

This work is supported by the NNSF(60474008) and the NSF of Shanghai City (03ZR14095), China

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Authors and Affiliations

  1. Dept. of Applied Mathematics, Tongji University, Shanghai, 200092, China

    Yinping Zhang & Jitao Sun

Authors
  1. Yinping Zhang
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  2. Jitao Sun
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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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Zhang, Y., Sun, J. (2005). Impulsive Robust Control of Interval Hopfield 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_34

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  • DOI: https://doi.org/10.1007/11427391_34

  • 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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