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Finite-time sampled-data synchronization for uncertain neutral-type semi-Markovian jump neural networks with mixed time-varying delays

Author

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  • Wang, Yao
  • Guo, Jun
  • Liu, Guobao
  • Lu, Junwei
  • Li, Fangyuan

Abstract

This paper addresses the finite-time synchronization problem for neutral-type semi-Markovian jump neural networks subject to random occurred uncertainties by sampled-data control approach. In order to deal with the influence of leakage delay and additive time delays on neutral-type neural networks, an appropriate Lyapunov–Krasovskii functional is employed. Some sufficient conditions are presented to guarantee the stochastic finite-time synchronization of the master system and slave system with an L2−L∞ performance level. In terms of linear matrix inequalities, the sampled-data controller gains are obtained. Two numerical examples are provided to demonstrate the effectiveness of our proposed method.

Suggested Citation

  • Wang, Yao & Guo, Jun & Liu, Guobao & Lu, Junwei & Li, Fangyuan, 2021. "Finite-time sampled-data synchronization for uncertain neutral-type semi-Markovian jump neural networks with mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    • Handle: RePEc:eee:apmaco:v:403:y:2021:i:c:s0096300321002873
    DOI: 10.1016/j.amc.2021.126197
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    References listed on IDEAS

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    1. Liang, Kun & Dai, Mingcheng & Shen, Hao & Wang, Jing & Wang, Zhen & Chen, Bo, 2018. "L2−L∞ synchronization for singularly perturbed complex networks with semi-Markov jump topology," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 450-462.
    2. Cao, Yang & Samidurai, R. & Sriraman, R., 2019. "Robust passivity analysis for uncertain neural networks with leakage delay and additive time-varying delays by using general activation function," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 57-77.
    3. Liu, Hailin & Chen, Guohua, 2007. "Delay-dependent stability for neural networks with time-varying delay," Chaos, Solitons & Fractals, Elsevier, vol. 33(1), pages 171-177.
    4. Vimal Kumar, S. & Raja, R. & Marshal Anthoni, S. & Cao, Jinde & Tu, Zhengwen, 2018. "Robust finite-time non-fragile sampled-data control for T-S fuzzy flexible spacecraft model with stochastic actuator faults," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 483-497.
    5. Wu, Tianyu & Huang, Xia & Chen, Xiangyong & Wang, Jing, 2020. "Sampled-data H∞ exponential synchronization for delayed semi-Markov jump CDNs: A looped-functional approach," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    6. Pradeep, C. & Cao, Yang & Murugesu, R. & Rakkiyappan, R., 2019. "An event-triggered synchronization of semi-Markov jump neural networks with time-varying delays based on generalized free-weighting-matrix approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 41-56.
    7. Hu, Xiaohui & Xia, Jianwei & Wei, Yunliang & Meng, Bo & Shen, Hao, 2019. "Passivity-based state synchronization for semi-Markov jump coupled chaotic neural networks with randomly occurring time delays," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 32-41.
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    Cited by:

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    3. Nguyen, Khanh Hieu & Kim, Sung Hyun, 2022. "Improved sampled-data control design of T-S fuzzy systems against mismatched fuzzy-basis functions," Applied Mathematics and Computation, Elsevier, vol. 428(C).

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