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State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays

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Abstract

The state estimation problem is considered for a class of discrete-time stochastic neural networks with Markovian jumping parameters in this paper. Norm-bounded parameter uncertainties in the state and measurement equation and time-varying delays are investigated. The neuron activation function satisfies sector-bounded conditions, and the nonlinear perturbation of the measurement equation satisfies standard Lipschitz condition and sector-bounded conditions. By constructing proper Lyapunov–Krasovskii functional, delay-dependent conditions are developed in terms of linear matrix inequalities (LMIs) to estimate the neuron state such that the dynamic of the estimation error system is asymptotically stable. Finally, numerical examples are shown to demonstrate the effectiveness and applicability of the proposed design method.

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Acknowledgments

The authors wish to thank the editor and the anonymous reviewers very much for their valuable comments and suggestions, which have led to significant improvements of the quality of this manuscript. This work was supported by the Natural Science Foundation of Jiangsu Province (No. BK20130239) and the Research Fund for the Doctoral Program of Higher Education of China (No. 20130094120015).

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Authors and Affiliations

  1. College of Internet of Things Engineering, Hohai University, Changzhou, 213022, China

    Mingang Hua, Huasheng Tan & Juntao Fei

  2. Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Changzhou, 213022, China

    Mingang Hua

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  1. Mingang Hua
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  3. Juntao Fei
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Correspondence to Mingang Hua.

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Hua, M., Tan, H. & Fei, J. State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays. Int. J. Mach. Learn. & Cyber. 8, 823–835 (2017). https://doi.org/10.1007/s13042-015-0373-2

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  • DOI: https://doi.org/10.1007/s13042-015-0373-2

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