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\(H_{\infty }\) State Estimation for Stochastic Jumping Neural Networks with Fading Channels Over a Finite-Time Interval

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Abstract

In the work, a class of Markov jump neural networks in the discrete-time domain with fading channels are taken into account. The main aim is to investigate the \(H_{\infty }\) state estimation issue when the Rice fading occurs in measured networks. In the first place, the analyses of the finite-time boundedness and the \(H_{\infty }\) performance for the estimation error system with the aid of finite-time stability theory are presented. Some conditions which guarantee the solvability of the addressed problem are established. Furthermore, by applying an unique decoupling method, the gains of the presented estimator are obtained under the feasible solutions of the conditions derived before. Finally, the validity of the presented approach is verified by a numerical example.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, 243002, China

    Liang Shen & Hao Shen

  2. School of Metallurgical Engineering, Anhui University of Technology, Ma’anshan, 243002, China

    Liang Shen & Hao Shen

  3. The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China

    Mingming Gao

  4. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China

    Yajuan Liu

  5. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Xia Huang

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  1. Liang Shen
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  2. Hao Shen
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  3. Mingming Gao
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  5. Xia Huang
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Correspondence to Mingming Gao.

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This work was supported by the National Natural Science Foundation of China (Nos. 61873002, 61703004, 61473178, 61573008), the National Natural Science Foundation of Anhui Province (Nos. 1708085MF165, 1808085QA18), and the China Postdoctoral Science Foundation (No. 2018M632206), the Natural Science Foundation of the Anhui Higher Education Institutions under grant KJ2017A064.

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Shen, L., Shen, H., Gao, M. et al. \(H_{\infty }\) State Estimation for Stochastic Jumping Neural Networks with Fading Channels Over a Finite-Time Interval. Neural Process Lett 50, 1–18 (2019). https://doi.org/10.1007/s11063-018-9907-4

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