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Passive state estimation for fuzzy jumping neural networks with fading channels based on the hidden Markov model

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  • Wang, Xuelian
  • Xia, Jianwei
  • Wang, Jing
  • Wang, Jian
  • Wang, Zhen

Abstract

The passive state estimation issue of the fuzzy Markov jump neural networks with fading channels is investigated in this paper. In view of the fact that it is difficult for the estimator to completely obtain the mode information of the neural networks in the actual situations, the hidden Markov model is utilized to depict the mode mismatching phenomenon between the networks and estimator. Moreover, aimed at the circumstance that data are transmitted over fading channels, and an improved discrete-time Rice fading model with the mode-dependent channel coefficients is employed. The principal goal is to devise a state estimator which can ensure that the error system realizes the stochastic stability and satisfies the passive performance index. By establishing a suitable mode-dependent Lyapunov–Krasovskii functional, sufficient conditions guaranteeing the realization of the designed state estimator are presented. The advantages and effectiveness of the adopted design scheme are verified by an illustrated example.

Suggested Citation

  • Wang, Xuelian & Xia, Jianwei & Wang, Jing & Wang, Jian & Wang, Zhen, 2019. "Passive state estimation for fuzzy jumping neural networks with fading channels based on the hidden Markov model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119314025
    DOI: 10.1016/j.physa.2019.122437
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    7. 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).
    8. Song, Xiaona & Wang, Mi & Song, Shuai & Wang, Zhen, 2021. "Observer-based sliding mode control for stochastic hyperbolic PDE systems with quantized output signal," Applied Mathematics and Computation, Elsevier, vol. 393(C).
    9. Fang, Tian & Jiao, Shiyu & Fu, Dongmei & Su, Lei, 2021. "Passivity-based synchronization for Markov switched neural networks with time delays and the inertial term," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    10. Suriguga, & Kao, Yonggui & Shao, Chuntao & Chen, Xiangyong, 2021. "Stability of high-order delayed Markovian jumping reaction-diffusion HNNs with uncertain transition rates," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    11. Liu, Xinmiao & Xia, Jianwei & Huang, Xia & Shen, Hao, 2020. "Generalized synchronization for coupled Markovian neural networks subject to randomly occurring parameter uncertainties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    12. Xin, Li-Ping & Yu, Bo & Zhao, Lin & Yu, Jinpeng, 2020. "Adaptive fuzzy backstepping control for a two continuous stirred tank reactors process based on dynamic surface control approach," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    13. 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).
    14. Kwon, W. & Jin, Yongsik & Lee, S.M., 2020. "PI-type event-triggered H∞ filter for networked T-S fuzzy systems using affine matched membership function approach," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    15. Cai, Xiao & Zhong, Shouming & Wang, Jun & Shi, Kaibo, 2020. "Robust H∞ control for uncertain delayed T-S fuzzy systems with stochastic packet dropouts," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    16. Han, Xin-Xin & Wu, Kai-Ning & Ding, Xiaohua, 2020. "Finite-time stabilization for stochastic reaction-diffusion systems with Markovian switching via boundary control," Applied Mathematics and Computation, Elsevier, vol. 385(C).

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