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Global exponential stability of a class of memristive neural networks with time-varying delays

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Abstract

This paper studies the uniqueness and global exponential stability of the equilibrium point for memristor-based recurrent neural networks with time-varying delays. By employing Lyapunov functional and theory of differential equations with discontinuous right-hand side, we establish several sufficient conditions for exponential stability of the equilibrium point. In comparison with the existing results, the proposed stability conditions are milder and more general, and can be applied to the memristor-based neural networks model whose connection weight changes continuously. Numerical examples are also presented to show the effectiveness of the theoretical results.

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Acknowledgments

This works is supported by the Graduate Innovation Foundation of Chongqing University Grant No. CDJXS12 18 00 05 and NPRP 4-1162- 1-181 funded by Qatar National Research Fund, Qatar.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Xin Wang & Chuandong Li

  2. Texas A&M University at Qatar, Doha, 23874, Qatar

    Tingwen Huang

  3. School of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Shukai Duan

Authors
  1. Xin Wang
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  2. Chuandong Li
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  3. Tingwen Huang
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  4. Shukai Duan
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Correspondence to Chuandong Li.

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Wang, X., Li, C., Huang, T. et al. Global exponential stability of a class of memristive neural networks with time-varying delays. Neural Comput & Applic 24, 1707–1715 (2014). https://doi.org/10.1007/s00521-013-1383-1

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  • DOI: https://doi.org/10.1007/s00521-013-1383-1

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