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State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach

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Abstract

In this paper, the sampled-data state estimation problem is investigated for neural networks with time-varying delays. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled data estimator is constructed. Based on the extended Wirtinger inequality, a discontinuous Lyapunov functional is introduced, which makes full use of the sawtooth structure characteristic of sampling input delay. New delay-dependent criteria are developed to estimate the neuron states through available output measurements such that the estimation error system is asymptotically stable. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulations are given to demonstrate the usefulness and effectiveness of the presented results.

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Acknowledgements

The work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (2010-0009373).

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Authors and Affiliations

  1. Nonlinear Dynamics Group, Department of Electrical Engineering/Information and Communication Engineering, Yeungnam University, 214-1 Dae-dong, Kyongsan, 712-749, Republic of Korea

    S. Lakshmanan, Ju H. Park & H. Y. Jung

  2. Department of Mathematics, Bharathiar University, Coimbatore, 641 046, Tamilnadu, India

    R. Rakkiyappan

Authors
  1. S. Lakshmanan
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  2. Ju H. Park
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  3. R. Rakkiyappan
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  4. H. Y. Jung
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Correspondence to Ju H. Park.

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Lakshmanan, S., Park, J.H., Rakkiyappan, R. et al. State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach. Nonlinear Dyn 73, 509–520 (2013). https://doi.org/10.1007/s11071-013-0805-z

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  • DOI: https://doi.org/10.1007/s11071-013-0805-z

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