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Multiple exponential stability for short memory fractional impulsive Cohen-Grossberg neural networks with time delays

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  • Zhang, Jinsen
  • Nie, Xiaobing

Abstract

Different from the existing multiple asymptotic stability or multiple Mittag-Leffler stability, the multiple exponential stability with explicit and faster convergence rate is addressed in this paper for short memory fractional-order impulsive Cohen-Grossberg neural networks with time delay. Firstly, ∏i=1n(2Hi+1) total equilibrium points of such n-neuron neural networks can be ensured via the known fixed point theorem. Then, by means of the theory of fractional-order differential equations, the methods of average impulsive interval and Lyapunov function, a series of sufficient conditions for determining the locally exponential stability of ∏i=1n(Hi+1) equilibrium points are obtained based on maximum norm, 1-norm and general q-norm (q=2n), respectively. This paper's research reveals the effects of impulsive function, impulsive interval, fractional order and time delay on the dynamic behaviors. Finally, four examples are proposed to demonstrate the effectiveness of theoretic achievements.

Suggested Citation

  • Zhang, Jinsen & Nie, Xiaobing, 2025. "Multiple exponential stability for short memory fractional impulsive Cohen-Grossberg neural networks with time delays," Applied Mathematics and Computation, Elsevier, vol. 486(C).
    • Handle: RePEc:eee:apmaco:v:486:y:2025:i:c:s0096300324005277
    DOI: 10.1016/j.amc.2024.129066
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    References listed on IDEAS

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    1. Tan, Manchun & Liu, Yunfeng & Xu, Desheng, 2019. "Multistability analysis of delayed quaternion-valued neural networks with nonmonotonic piecewise nonlinear activation functions," Applied Mathematics and Computation, Elsevier, vol. 341(C), pages 229-255.
    2. Nie, Xiaobing & Liang, Jinling & Cao, Jinde, 2019. "Multistability analysis of competitive neural networks with Gaussian-wavelet-type activation functions and unbounded time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 356(C), pages 449-468.
    3. Yang, Xujun & Wu, Xiang & Song, Qiankun, 2024. "Caputo−Wirtinger integral inequality and its application to stability analysis of fractional-order systems with mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 460(C).
    4. Zhang, Lingzhong & Yang, Yongqing & Xu, Xianyun, 2018. "Synchronization analysis for fractional order memristive Cohen–Grossberg neural networks with state feedback and impulsive control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 644-660.
    5. Hongguang Fan & Yue Rao & Kaibo Shi & Hui Wen, 2023. "Global Synchronization of Fractional-Order Multi-Delay Coupled Neural Networks with Multi-Link Complicated Structures via Hybrid Impulsive Control," Mathematics, MDPI, vol. 11(14), pages 1-17, July.
    6. Li, Hong-Li & Hu, Cheng & Zhang, Long & Jiang, Haijun & Cao, Jinde, 2021. "Non-separation method-based robust finite-time synchronization of uncertain fractional-order quaternion-valued neural networks," Applied Mathematics and Computation, Elsevier, vol. 409(C).
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