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Mean square exponential stability for stochastic memristor-based neural networks with leakage delay

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  • Wang, Fen
  • Chen, Yuanlong

Abstract

Under the framework of Filippov solutions, the issues of mean square exponential stability for stochastic memristor-based neural networks with leakage delay in this paper are studied. By constructing a suitable Lyapunov–Krasovskii functional and using Itô,s differential formula, Lemma of Schur complement and linear matrix inequality technique, the criteria are derived. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of the MATLAB toolbox. Compared with previous results, the activation function's boundedness, differentiability and monotonicity are not required. Finally, three numerical examples are provided to illustrate the effectiveness of the proposed results.

Suggested Citation

  • Wang, Fen & Chen, Yuanlong, 2021. "Mean square exponential stability for stochastic memristor-based neural networks with leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:chsofr:v:146:y:2021:i:c:s0960077921001636
    DOI: 10.1016/j.chaos.2021.110811
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    Cited by:

    1. Yan, Hongyun & Qiao, Yuanhua & Duan, Lijuan & Miao, Jun, 2022. "New results of quasi-projective synchronization for fractional-order complex-valued neural networks with leakage and discrete delays," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. 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).

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