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Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse

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  • Wang, Yuxiao
  • Cao, Yuting
  • Guo, Zhenyuan
  • Wen, Shiping

Abstract

The research direction of this paper is passivity and passification of memristive recurrent neural networks (MRNNs) with multi-proportional delays and impulse. Preparing for passive analysis, the model of MRNNs is transformed into the general recurrent neural networks (RNNs) model through the way of non-smooth analysis. Utilizing the proper Lyapunov–Krasovskii functions constructed in this paper and the common matrix inequalities technique, a novel condition is derived which is sufficient to make sure that system is passive. In addition, it relaxes the condition that the symmetric matrices are all required to be positive definite. The final results are presented by linear matrix inequalities (LMIs) and its verification is easy to be carried out by the LMI toolbox. And there are several numerical examples showing the effectiveness and correctness of the derived criteria.

Suggested Citation

  • Wang, Yuxiao & Cao, Yuting & Guo, Zhenyuan & Wen, Shiping, 2020. "Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:apmaco:v:369:y:2020:i:c:s0096300319308306
    DOI: 10.1016/j.amc.2019.124838
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    References listed on IDEAS

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    Cited by:

    1. Li, Yongkun & Wang, Xiaohui, 2021. "Almost periodic solutions in distribution of Clifford-valued stochastic recurrent neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).

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