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H∞ synchronization of semi-Markovian jump neural networks with random sensor nonlinearities via adaptive event-triggered output feedback control

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  • Song, Xingxing
  • Lu, Hongqian
  • Xu, Yao
  • Zhou, Wuneng

Abstract

In this paper, the H∞ synchronization problem of semi-Markovian jump neural networks (s-MJNNs) based on adaptive event-triggered scheme (AETS) output feedback control is studied. Also, the time-varying delay and leakage delay are considered in the system model. In order to solve the problems that the state of the system is not completely observable and limited network resources, an output feedback controller with AETS is designed. At the same time, in order to describe the performance of the sensor in the feedback link, the random nonlinear phenomenon of the sensor is described by the variable complying with Bernoulli probability distribution. A suitable Lyapunov–Krasovskii functional (LKF) is constructed and the bounds of integral terms are estimated by affine Bessel–Legendre inequality. Finally, sufficient conditions for asymptotic stability of the synchronization error system are obtained. And, two numerical examples show the feasibility of the research work.

Suggested Citation

  • Song, Xingxing & Lu, Hongqian & Xu, Yao & Zhou, Wuneng, 2022. "H∞ synchronization of semi-Markovian jump neural networks with random sensor nonlinearities via adaptive event-triggered output feedback control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 198(C), pages 1-19.
    • Handle: RePEc:eee:matcom:v:198:y:2022:i:c:p:1-19
    DOI: 10.1016/j.matcom.2022.02.008
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    References listed on IDEAS

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    1. Wang, Xuelian & Xia, Jianwei & Wang, Jing & Wang, Zhen & Wang, Jian, 2020. "Reachable set estimation for Markov jump LPV systems with time delays," Applied Mathematics and Computation, Elsevier, vol. 376(C).
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    3. 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).
    4. Yujiao Liu & Rongqiang Tang & Chao Zhou & Zhengrong Xiang & Xinsong Yang, 2020. "Event-triggered leader-following consensus of multiple mechanical systems with switched dynamics," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(16), pages 3563-3572, December.
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    6. Song, Xiaona & Men, Yunzhe & Zhou, Jianping & Zhao, Junjie & Shen, Hao, 2017. "Event-triggered H∞ control for networked discrete-time Markov jump systems with repeated scalar nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 298(C), pages 123-132.
    7. 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.
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    Cited by:

    1. Priyanka, K. Sri Raja & Soundararajan, G. & Kashkynbayev, Ardak & Nagamani, G., 2023. "Exponential H∞ synchronization and anti-synchronization of delayed discrete-time complex-valued neural networks with uncertainties," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 301-321.
    2. Zhao, Rui & Wang, Baoxian & Jian, Jigui, 2022. "Global μ-stabilization of quaternion-valued inertial BAM neural networks with time-varying delays via time-delayed impulsive control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 223-245.
    3. Saravanan Shanmugam & Rajarathinam Vadivel & Nallappan Gunasekaran, 2023. "Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation," Mathematics, MDPI, vol. 11(10), pages 1-24, May.

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