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Asynchronous finite-time extended dissipative sliding mode control for semi-Markovian jump master–slave neural networks

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  • Cheng, Guifang
  • Liu, Hao

Abstract

This paper aims to investigate the finite-time extended dissipative asynchronous sliding mode control (SMC) on semi-Markovian jump master–slave neural networks (MSNNs) subject to event-triggering scheme (ETS) and packet dropout. Considering ETS, packet dropout and network-induced delays, the asynchronous finite-time SMC which is more realistic is designed. Sufficient conditions are obtained that closed-loop system is extended dissipative stochastic finite-time bound. Meantime, we present novel results for closed-loop system on extended dissipative stochastic finite-time contractive bound. Finally, the biological neural network is presented to illustrate the validness of methods.

Suggested Citation

  • Cheng, Guifang & Liu, Hao, 2024. "Asynchronous finite-time extended dissipative sliding mode control for semi-Markovian jump master–slave neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    • Handle: RePEc:eee:chsofr:v:179:y:2024:i:c:s0960077924000080
    DOI: 10.1016/j.chaos.2024.114457
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    References listed on IDEAS

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    2. Guo, Runan & Xu, Shengyuan, 2023. "Observer-based sliding mode synchronization control of complex-valued neural networks with inertial term and mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    3. Zheng, Wei & Zhang, Zhiming & Lam, Hak-Keung & Sun, Fuchun & Wen, Shuhuan, 2023. "LMIs-based exponential stabilization for interval delay systems via congruence transformation: Application in chaotic Lorenz system," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
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