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LMI-Based Synchronization Conditions to R-L Fractional Time-Varying Delayed Neural Networks with Parametric Uncertainty

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Abstract

The synchronization conditions to Riemann-Liouville fractional time-varying delayed neural networks with parametric uncertainty are investigated in this article. A novel Lyapunov-Krasovskii functional including double integral terms is proposed, which greatly cutes down on the conservatism about the results. The new synchronization criteria are established and characterized as linear matrix inequalities by using convex combination technique, fractional calculus properties and delay-decomposing method. Finally, two numerical simulations illustrate the correctness of the results.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61833005), the Natural Science Foundation of Anhui Province of China (1908085MA01), the Natural Science Foundation of the Higher Education Institutions of Anhui Province (KJ2019A0573) and the Top Young Talents Program of Higher Learning Institutions of Anhui Province of China (gxyq2019048).

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Authors and Affiliations

  1. School of Mathematics and Physics, Anqing Normal University, Anqing, 246133, People’s Republic of China

    Hongmei Zhang, Hai Zhang & Weiwei Zhang

  2. School of Mathematics, Southeast University, Nanjing, 211189, People’s Republic of China

    Jinde Cao

  3. Yonsei Frontier Lab, Yonsei University, Seoul, 03722, South Korea

    Jinde Cao

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  1. Hongmei Zhang
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  2. Hai Zhang
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  3. Weiwei Zhang
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  4. Jinde Cao
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Correspondence to Hai Zhang.

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Zhang, H., Zhang, H., Zhang, W. et al. LMI-Based Synchronization Conditions to R-L Fractional Time-Varying Delayed Neural Networks with Parametric Uncertainty. Neural Process Lett 55, 4031–4045 (2023). https://doi.org/10.1007/s11063-022-11026-x

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