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Sampled-data state estimation for delayed neural networks with Markovian jumping parameters

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Abstract

This paper is concerned with the sampled-data state estimation problem for a class of delayed neural networks with Markovian jumping parameters. Unlike the classical state estimation problem, in our state estimation scheme, the sampled measurements are adopted to estimate the concerned neuron states. The neural network under consideration is assumed to have multiple modes that switch from one to another according to a given Markovian chain. By utilizing the input delay approach, the sampling period is converted into a time-varying yet bounded delay. Then a sufficient condition is given under which the resulting error dynamics of the neural networks is exponentially stable in the mean square. Based on that, a set of sampled-data estimators is designed in terms of the solution to a set of linear matrix inequalities (LMIs) which can be solved by using the available software. Finally, a numerical example is used to show the effectiveness of the estimation approach proposed in this paper.

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Acknowledgements

This work was supported by the Research Project of Zhoushan Science and Technology Bureau (2011C12041).

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Authors and Affiliations

  1. College of Electromechanical Engineering, Zhejiang Ocean University, Zhoushan, 316004, China

    Jiawen Hu

  2. School of Information Science and Technology, Donghua University, Shanghai, 200051, China

    Nan Li

  3. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xiaohui Liu & Gongxuan Zhang

Authors
  1. Jiawen Hu
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  2. Nan Li
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  3. Xiaohui Liu
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  4. Gongxuan Zhang
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Correspondence to Jiawen Hu.

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Hu, J., Li, N., Liu, X. et al. Sampled-data state estimation for delayed neural networks with Markovian jumping parameters. Nonlinear Dyn 73, 275–284 (2013). https://doi.org/10.1007/s11071-013-0783-1

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