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Stability analysis for memristor-based stochastic multi-layer neural networks with coupling disturbance

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  • Xiang, Jianglian
  • Ren, Junwu
  • Tan, Manchun

Abstract

This paper discusses the asymptotical synchronization and the input-to-state exponential stability for memrist or-based multi-layers networks with delays under the coupling disturbance and stochastic noise. First, in order to solve the nonlinear coupling function of mismatched parameter and discontinuous activation in the system, methods of differential inclusion and Laplace transform are used. Then, based on the Lyapunov–Krasovskii functional, technique of inequality and linear matrix inequality, new sufficient conditions are also derived, in order to ensure the asymptotic synchronization and the input-to-state exponential stability of the considered model. Finally, two examples and simulations are given to illustrate the validity and correctness of our conclusions.

Suggested Citation

  • Xiang, Jianglian & Ren, Junwu & Tan, Manchun, 2022. "Stability analysis for memristor-based stochastic multi-layer neural networks with coupling disturbance," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p1:s096007792200950x
    DOI: 10.1016/j.chaos.2022.112771
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    References listed on IDEAS

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    Cited by:

    1. Shi, Lingna & Li, Jiarong & Jiang, Haijun & Wang, Jinling, 2023. "Quasi-synchronization of multi-layer delayed neural networks with parameter mismatches via impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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