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Impulsive Stabilization and Synchronization of Fractional-Order Complex-Valued Neural Networks

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Abstract

This paper focuses on the impulsive stabilization of fractional-order complex-valued neural networks. Based on impulsive control and some fractional-order differential inequalities, some valid criteria are achieved to ensure the global asymptotic stabilization of the addressed networks. The maximal impulsive strength and the maximal impulsive interval are also given. Under certain conditions, some sufficient conditions are derived to ensure the global \(\alpha \)-exponential stability of the equilibrium point. Compared to the traditional linear feedback control, the impulsive control strategy only needs small control gains and shorter time to achieve global stabilization. When employing the impulsive control to the error system, a parallel criterion regarding to the complete synchronization of the drive-response systems is also generated. The effectiveness and advantages of the proposed methods are confirmed through simulation results.

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Acknowledgements

The authors are grateful for the support of the National Natural Science Foundation of China under Grant 11601268.

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Authors and Affiliations

  1. College of Science, China Three Gorges University, Yichang, 443002, Hubei, China

    Peng Wan & Jigui Jian

  2. Three Gorges Mathematical Research Center, China Three Gorges University, Yichang, 443002, Hubei, China

    Jigui Jian

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  1. Peng Wan
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Correspondence to Jigui Jian.

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Wan, P., Jian, J. Impulsive Stabilization and Synchronization of Fractional-Order Complex-Valued Neural Networks. Neural Process Lett 50, 2201–2218 (2019). https://doi.org/10.1007/s11063-019-10002-2

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