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Finite-time synchronization analysis for the generalized Caputo fractional spatio-temporal neural networks

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  • Liu, Xianghu
  • Li, Yanfang
  • Xu, Guangjun

Abstract

This paper is concerned with finite-time synchronization analysis for the generalized Caputo fractional spatio-temporal neural networks with time delay(GCFSTNN). The generalized Caputo type fractional derivative are defined, two novel generalized Caputo fractional differential inequalities are proved. Meanwhile, some control strategies are designed to get the finite-time synchronization results. Finally, numerical examples and simulation results are showed to demonstrate validation of finite-time synchronization conditions.

Suggested Citation

  • Liu, Xianghu & Li, Yanfang & Xu, Guangjun, 2025. "Finite-time synchronization analysis for the generalized Caputo fractional spatio-temporal neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 230(C), pages 94-110.
    • Handle: RePEc:eee:matcom:v:230:y:2025:i:c:p:94-110
    DOI: 10.1016/j.matcom.2024.11.006
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    References listed on IDEAS

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    1. Li, Hong-Li & Cao, Jinde & Jiang, Haijun & Alsaedi, Ahmed, 2019. "Finite-time synchronization and parameter identification of uncertain fractional-order complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
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    3. Zhang, Lei & Song, Qiankun & Zhao, Zhenjiang, 2017. "Stability analysis of fractional-order complex-valued neural networks with both leakage and discrete delays," Applied Mathematics and Computation, Elsevier, vol. 298(C), pages 296-309.
    4. Baleanu, Dumitru & Wu, Guo–Cheng & Zeng, Sheng–Da, 2017. "Chaos analysis and asymptotic stability of generalized Caputo fractional differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 102(C), pages 99-105.
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