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Exponential Synchronization of Complex-Valued Neural Networks Via Average Impulsive Interval Strategy

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Abstract

In this paper, the issue of the exponential synchronization for complex-valued neural networks with both discrete and distributed delays is investigated by applying impulsive control protocol. Based on the Lyapunov–Krasovskii function, average impulsive interval as well as the comparison principle, some simple verifiable sufficient criteria are established to guarantee the exponential synchronization between the master and the slave systems. Meanwhile, through the serious analysis of the networks systems, the exponential convergence rate can be specified. Additionally, a numerical example is finally given to illustrate the effectiveness of the proposed theoretical results.

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Acknowledgements

This work was supported by the Research Fund Project of Zhoukou Normal University (Grant No. ZKNUC 2018009), the Scientific Research Program of the Higher Education Institution of Xinjiang (Grants Nos. XJEDU2017T001, XJEDU2018Y004), the Doctoral Foundation of Xinjiang University (Grant No. 62008032), and the National Natural Science Foundation of China (Grants Nos. U1703262, 61563048, 11702237), the Tianshan Xuesong Program (Grant No. 2018XS02).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou, 466001, Henan, People’s Republic of China

    Mei Liu & Zhanfeng Li

  2. College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, People’s Republic of China

    Haijun Jiang, Cheng Hu & Zhiyong Yu

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  1. Mei Liu
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  2. Zhanfeng Li
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  3. Haijun Jiang
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  4. Cheng Hu
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  5. Zhiyong Yu
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Correspondence to Mei Liu.

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Liu, M., Li, Z., Jiang, H. et al. Exponential Synchronization of Complex-Valued Neural Networks Via Average Impulsive Interval Strategy. Neural Process Lett 52, 1377–1394 (2020). https://doi.org/10.1007/s11063-020-10309-5

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