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Synchronization analysis and parameters identification of uncertain delayed fractional-order BAM neural networks

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Abstract

In this paper, synchronization analysis and parameters identification issues are explored for uncertain delayed fractional-order BAM neural networks. By designing pertinent state feedback control strategies and parameters updated laws, some ample criteria are procured for ensuring the finite-time synchronization and the Mittag-Leffler synchronization of the considered networks via exploiting the Lyapunov function theory, fractional calculus theory and inequality analysis techniques, meanwhile, the settling time of finite-time synchronization is given, which relates to the initial values. Moreover, parameters identification is actualized triumphantly for uncertain or unknown parameters. Finally, numerical examples are provided to show the availability of the theoretical results.

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Acknowledgements

This work is supported by the Tianshan Youth Program-Training Program for Excellent Young Scientific and Technological Talents (Grant No. 2019Q017), Project Funded by China Postdoctoral Science Foundation (Grant No. 2018M632205), the Scientific Research Program of the Higher Education Institution of Xinjiang (Grant Nos. XJEDU2017S001, XJEDU2021I002), the National Natural Science Foundation of China (Grant Nos. 11702237, 11861065, 61866036, 61963033).

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Authors and Affiliations

  1. College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830017, China

    Juanping Yang, Hong-Li Li, Long Zhang, Cheng Hu & Haijun Jiang

  2. School of Mathematics, Southeast University, Nanjing, 210096, China

    Hong-Li Li

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Yang, J., Li, HL., Zhang, L. et al. Synchronization analysis and parameters identification of uncertain delayed fractional-order BAM neural networks. Neural Comput & Applic 35, 1041–1052 (2023). https://doi.org/10.1007/s00521-022-07791-4

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