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Non-fragile finite-time l2−l∞ state estimation for discrete-time Markov jump neural networks with unreliable communication links

Author

Listed:
  • Li, Feng
  • Shen, Hao
  • Chen, Mengshen
  • Kong, Qingkai

Abstract

This paper is concerned with the problem of finite-time l2−l∞ non-fragile state estimation for discrete-time Markov jump neural networks with unreliable communication links. The simultaneous occurrences of packet dropouts, time delays and the sensor nonlinearity stemmed from the unreliable communication links are considered. The focus is on the design of non-fragile state estimator such that the augmented estimation error system is mean-square stochastically finite-time stable with a prescribed level of l2−l∞ performance. By employing Lyapunov–Krasovskii approach and finite-time analysis theory, some sufficient conditions have been obtained for the existence of an admissible state estimator. Finally, a numerical example is employed to demonstrate the effectiveness of our proposed approach.

Suggested Citation

  • Li, Feng & Shen, Hao & Chen, Mengshen & Kong, Qingkai, 2015. "Non-fragile finite-time l2−l∞ state estimation for discrete-time Markov jump neural networks with unreliable communication links," Applied Mathematics and Computation, Elsevier, vol. 271(C), pages 467-481.
    • Handle: RePEc:eee:apmaco:v:271:y:2015:i:c:p:467-481
    DOI: 10.1016/j.amc.2015.09.029
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    Citations

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    Cited by:

    1. Fang, Liandi & Ma, Li & Ding, Shihong & Zhao, Dean, 2019. "Finite-time stabilization for a class of high-order stochastic nonlinear systems with an output constraint," Applied Mathematics and Computation, Elsevier, vol. 358(C), pages 63-79.
    2. Jiao, Shiyu & Shen, Hao & Wei, Yunliang & Huang, Xia & Wang, Zhen, 2018. "Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 338-350.
    3. Gao, Xianwen & He, Hangfeng & Qi, Wenhai, 2017. "Admissibility analysis for discrete-time singular Markov jump systems with asynchronous switching," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 431-441.
    4. Lee, Tae H. & Park, Ju H. & Jung, Hoyoul, 2018. "Network-based H∞ state estimation for neural networks using imperfect measurement," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 205-214.
    5. Li, Xin & Wei, Guoliang & Ding, Derui, 2021. "Distributed resilient interval estimation for sensor networks under aperiodic denial-of-service attacks and adaptive event-triggered protocols," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    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. Shen, Mouquan & Ye, Dan, 2017. "A finite frequency approach to control of Markov jump linear systems with incomplete transition probabilities," Applied Mathematics and Computation, Elsevier, vol. 295(C), pages 53-64.
    8. 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.
    9. Jiang, Tingting & Zhang, Yuping & Zeng, Yong & Zhong, Shouming & Shi, Kaibo & Cai, Xiao, 2021. "Finite-time analysis for networked predictive control systems with induced time delays and data packet dropouts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    10. Zhai, Ding & Lu, An-Yang & Dong, Jiuxiang & Zhang, Qing-Ling, 2016. "Asynchronous H∞ filtering for 2D discrete Markovian jump systems with sensor failure," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 60-79.
    11. Li, Feng & Song, Shuai & Zhao, Jianrong & Xu, Shengyuan & Zhang, Zhengqiang, 2019. "Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 1-13.

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