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A new method to study global exponential stability of inertial neural networks with multiple time-varying transmission delays

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  • Chang, Shuang
  • Wang, Yantao
  • Zhang, Xian
  • Wang, Xin

Abstract

In this article, the global exponential stability (GES) of inertial neural networks (INNs) are directly analyzed by proposing a new parameterized method. The parameterized representations of the states of neurons and their derivatives in the considered INNs are first given by introducing the relevant parameters. Furthermore, the sufficient conditions for the GES of the considered INNs are obtained by using the inequality technique. The obtained stability conditions consist of only a few simple linear scalar inequalities (LSIs) which are convenient to solve. Different from the previous works, the derived GES criteria do not involve any model transformation and any Lyapunov–Krasovskii functional (LKF), which reduces the computational complexity and simplifies the theoretical analysis. The last, a numerical simulation is presented to demonstrate the effectiveness of proposed parameterized method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:211:y:2023:i:c:p:329-340
    DOI: 10.1016/j.matcom.2023.04.008
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    References listed on IDEAS

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    1. Huang, Chuangxia & Su, Renli & Cao, Jinde & Xiao, Songlin, 2020. "Asymptotically stable high-order neutral cellular neural networks with proportional delays and D operators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 127-135.
    2. Wang, Junlan & Wang, Xin & Wang, Yantao & Zhang, Xian, 2021. "Non-reduced order method to global h-stability criteria for proportional delay high-order inertial neural networks," Applied Mathematics and Computation, Elsevier, vol. 407(C).
    3. Yaning Yu & Ziye Zhang, 2022. "State Estimation for Complex-Valued Inertial Neural Networks with Multiple Time Delays," Mathematics, MDPI, vol. 10(10), pages 1-14, May.
    4. Chen, Yonghui & Zhang, Xian & Xue, Yu, 2022. "Global exponential synchronization of high-order quaternion Hopfield neural networks with unbounded distributed delays and time-varying discrete delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 173-189.
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    Cited by:

    1. Juan Yu & Kailong Xiong & Cheng Hu, 2024. "Synchronization Analysis for Quaternion-Valued Delayed Neural Networks with Impulse and Inertia via a Direct Technique," Mathematics, MDPI, vol. 12(7), pages 1-22, March.
    2. Arunagirinathan, S. & Lee, T.H., 2024. "Generalized delay-dependent reciprocally convex inequality on stability for neural networks with time-varying delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 109-120.

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