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Finite-time anti-synchronization and fixed-time quasi-anti-synchronization for complex-valued neural networks with time-varying delay and application

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Abstract

In this article, the issue of the finite-time anti-synchronization and fixed-time quasi-anti-synchronization for complex-valued neural networks with time-varying delays and leakage delay is explored. First of all, the model is divided into two equivalent real-valued neural networks employing the decomposing technique. Second, by utilizing the Hölder inequality, constructed function and new designed quantized controller, several novel sufficient criteria are obtained to ensure finite-time anti-synchronization of the studied system. In addition, a feedback control strategy is developed to derive some conclusions of quasi-anti-synchronization by applying the Lyapunov function. Meanwhile, the estimated settling time is acquired which does not rely on the initial value. Two methods our paper used to analyze can provide new ideas for future research. Finally, numerical simulation with simulation results is offered to indicate the efficiency and validity of the proposed theoretical results, and the results are applied to the field of image encryption.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (No. 2020YFC1512002), in part by the Technology Innovation Leading Program of Shaanxi (No. 2020QFY03-01) and in part by the Technology Program of WeiNan (2020ZDYFGYCX-81).

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Authors and Affiliations

  1. School of Electronic and Control, Chang’an University, Xi’an, 710064, Shaanxi, China

    Meng Hui, JiaHuang Zhang, Ning Yao & Weizhe Wu

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  1. Meng Hui
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  2. JiaHuang Zhang
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  4. Weizhe Wu
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Correspondence to Meng Hui.

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Hui, M., Zhang, J., Yao, N. et al. Finite-time anti-synchronization and fixed-time quasi-anti-synchronization for complex-valued neural networks with time-varying delay and application. Neural Comput & Applic 35, 15775–15790 (2023). https://doi.org/10.1007/s00521-023-08474-4

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