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Neural Computing and Applications

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24.09.2022 | Original Article

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

verfasst von: Juanping Yang, Hong-Li Li, Long Zhang, Cheng Hu, Haijun Jiang

Erschienen in: Neural Computing and Applications | Ausgabe 1/2023

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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.

Vorheriger Artikel Linear normalization attention neural Hawkes process

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Titel: Synchronization analysis and parameters identification of uncertain delayed fractional-order BAM neural networks
verfasst von: Juanping Yang
Hong-Li Li
Long Zhang
Cheng Hu
Haijun Jiang
Publikationsdatum: 24.09.2022
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07791-4

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