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Impulsive stabilization and synchronization of Hopfield-type neural networks with impulse time window

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Abstract

This paper studies the problem of global exponential stabilization and synchronization for impulsive Hopfield-type neural networks with impulse time window. By using the stability theory of impulsive dynamical systems, some sufficient conditions guaranteeing the global exponential stabilization and synchronization of Hopfield-type NNs are derived. The main innovation embodies that the impulsive instants are no longer limited at fixed instants, but suggested to be at some certain time intervals, named by impulse time windows. We shall show that impulses occurring randomly in impulse time windows can still stabilize and/or synchronize the considered neural networks under certain suitable assumptions. Two numerical examples are also given to illustrate the effectiveness of theoretical results.

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Acknowledgments

This research is supported by the Natural Science Foundation of China (Grant No. 63174078), the Fundamental Research Funds for the Central Universities (XDJK2012C069), and NPRP grant # NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation).

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Yinghua Zhou, Chuandong Li & Xin Wang

  2. Department of Mathematics, Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

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  1. Yinghua Zhou
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  2. Chuandong Li
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  3. Tingwen Huang
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  4. Xin Wang
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Correspondence to Chuandong Li.

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Zhou, Y., Li, C., Huang, T. et al. Impulsive stabilization and synchronization of Hopfield-type neural networks with impulse time window. Neural Comput & Applic 28, 775–782 (2017). https://doi.org/10.1007/s00521-015-2105-7

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  • DOI: https://doi.org/10.1007/s00521-015-2105-7

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