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The characteristics method to study global exponential stability of delayed inertial neural networks

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  • Wang, Wentao
  • Wu, Jihui
  • Chen, Wei

Abstract

In this paper, we address the issue of global exponential stability for a class of delayed inertial neural networks (DINNs). Employing the characteristics method, we derive several sufficient conditions, which are both decay and delay-dependent as well as decay and delay-independent, to guarantee the global exponential stability of the given neural networks. Lastly, we present three numerical examples to highlight the advantages of our novel results.

Suggested Citation

  • Wang, Wentao & Wu, Jihui & Chen, Wei, 2025. "The characteristics method to study global exponential stability of delayed inertial neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 232(C), pages 91-101.
    • Handle: RePEc:eee:matcom:v:232:y:2025:i:c:p:91-101
    DOI: 10.1016/j.matcom.2024.12.021
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    References listed on IDEAS

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    1. Chunguang Li & Guangrong Chen & Xiaofeng Liao & Juebang Yu, 2004. "Hopf bifurcation and chaos in a single inertial neuron model with time delay," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 41(3), pages 337-343, October.
    2. Deyi Li & Yuanyuan Wang & Guici Chen & Shasha Zhu, 2018. "Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, October.
    3. Chang, Shuang & Wang, Yantao & Zhang, Xian & Wang, Xin, 2023. "A new method to study global exponential stability of inertial neural networks with multiple time-varying transmission delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 211(C), pages 329-340.
    4. Zhang, Guodong & Cao, Jinde, 2023. "New results on fixed/predefined-time synchronization of delayed fuzzy inertial discontinuous neural networks: Non-reduced order approach," Applied Mathematics and Computation, Elsevier, vol. 440(C).
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    Cited by:

    1. Jaeseung Kim & Hwijae Son, 2025. "Causality-Aware Training of Physics-Informed Neural Networks for Solving Inverse Problems," Mathematics, MDPI, vol. 13(7), pages 1-23, March.

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