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Robust exponential stabilization for uncertain neutral neural networks with interval time-varying delays by periodically intermittent control

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Abstract

This paper studies the robust exponential stabilization for a class of uncertain neutral neural networks with mixed interval time-varying delays. The aim of the paper is to design periodically intermittent control such that the closed-loop system is exponentially stable. By constructing a suitable Lyapunov–Krasovskii functional and by using some useful lemmas and some new analysis techniques, the researchers generate novel exponential stabilization criteria to ensure the robust exponential stabilization of considered uncertain neutral neural networks in terms of linear matrix inequalities. Based on the proposed criteria, an intermittent state-feedback controller design approach is introduced. Some numerical examples are given to show the effectiveness and benefits of the theoretical results.

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Acknowledgements

This work is supported in part by the Natural Science Foundation of China under Grants 61703307 and 61603272.

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Authors and Affiliations

  1. School of Science, Tianjin Polytechnic University, Tianjin, 300387, China

    Yali Dong, Liangliang Guo & Jing Hao

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  1. Yali Dong
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  2. Liangliang Guo
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  3. Jing Hao
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Correspondence to Yali Dong.

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The authors declared that they have no conflict of interest.

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Dong, Y., Guo, L. & Hao, J. Robust exponential stabilization for uncertain neutral neural networks with interval time-varying delays by periodically intermittent control. Neural Comput & Applic 32, 2651–2664 (2020). https://doi.org/10.1007/s00521-018-3671-2

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  • DOI: https://doi.org/10.1007/s00521-018-3671-2

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