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Finite-Time \(L_\infty \) Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals

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Abstract

This paper, by proposing a sampled-data control scheme, we investigate the finite-time \(L_\infty \) performance state estimation of recurrent neural networks. By constructing a novel Lyapunov functional, new stability and stabilization conditions are derived. By utilizing integral inequality techniques, sufficient LMI conditions are derived to ensure the finite-time stability of considered neural networks. Furthermore, finite-time observer gain analysis of recurrent neural networks is set up to measure its disturbance tolerance capability in the fixed time interval. Numerical examples are given to verify the effectiveness of the proposed approach.

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Acknowledgements

The work of author was supported by CSIR. 25(0274)/17/EMR-II dated 27/04/2017.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama, 337-8570, Japan

    N. Gunasekaran

  2. Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, 632115, India

    M. Syed Ali & S. Pavithra

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  1. N. Gunasekaran
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  2. M. Syed Ali
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  3. S. Pavithra
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Correspondence to M. Syed Ali.

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Gunasekaran, N., Ali, M.S. & Pavithra, S. Finite-Time \(L_\infty \) Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals. Neural Process Lett 51, 1379–1392 (2020). https://doi.org/10.1007/s11063-019-10114-9

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