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Non-fragile asynchronous state estimation for Markovian switching CVNs with partly accessible mode detection: The discrete-time case

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  • Li, Qiang
  • Liang, Jinling

Abstract

This article is devoted to the non-fragile asynchronous state estimation problem for Markovian switching complex-valued networks subject to randomly occurring nonlinearities (RONs) and external stochastic disturbances. Two mutually uncorrelated random variables with known statistical properties are introduced to depict the existing RONs. Considering the case that the information concerning to the system modes cannot be completely acquired by the state estimator to be designed, the asynchronous phenomenon is considered via a hidden Markov model with only partial mode detection probabilities being accessible. By resorting to the intensive stochastic analysis as well as an improved complex-valued reciprocal convex inequality, some mode-dependent criteria are provided which ascertain that the estimation error system is asymptotically mean square stable. In addition, the estimator gains desired can be appropriately designed by resorting to feasible solutions of a set of complex matrix inequalities. One numerical example is also provided to demonstrate effectiveness of the estimation scheme proposed.

Suggested Citation

  • Li, Qiang & Liang, Jinling, 2022. "Non-fragile asynchronous state estimation for Markovian switching CVNs with partly accessible mode detection: The discrete-time case," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    • Handle: RePEc:eee:apmaco:v:412:y:2022:i:c:s0096300321006676
    DOI: 10.1016/j.amc.2021.126583
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    References listed on IDEAS

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    1. Yun Chen & Zhangping Chen & Zhenyu Chen & Anke Xue, 2020. "Observer-based passive control of non-homogeneous Markov jump systems with random communication delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(6), pages 1133-1147, April.
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    5. Qiang Li & Jinling Liang, 2020. "Dissipativity of the stochastic Markovian switching CVNNs with randomly occurring uncertainties and general uncertain transition rates," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(6), pages 1102-1118, April.
    6. Hu, Binxin & Song, Qiankun & Zhao, Zhenjiang, 2020. "Robust state estimation for fractional-order complex-valued delayed neural networks with interval parameter uncertainties: LMI approach," Applied Mathematics and Computation, Elsevier, vol. 373(C).
    7. Shi, Shuang & Fei, Zhongyang & Shi, Zhenpeng & Ren, Shunqing, 2018. "Stability and stabilization for discrete-time switched systems with asynchronism," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 520-536.
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    Cited by:

    1. Liu, Mengmeng & Yu, Jinyong & Liu, Yu, 2022. "Dynamic event-triggered asynchronous fault detection for Markov jump systems with partially accessible hidden information and subject to aperiodic DoS attacks," Applied Mathematics and Computation, Elsevier, vol. 431(C).

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