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Non-reduced order method to global h-stability criteria for proportional delay high-order inertial neural networks

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  • Wang, Junlan
  • Wang, Xin
  • Wang, Yantao
  • Zhang, Xian

Abstract

This article mainly explores the global h-stability for proportional delay high-order inertial neural networks. Without adopting reduced order method, a new Lyapunov–Krasovskii functional is constructed to derive the delay-dependent global h-stability criterion, which is new and improves some previous works. Moreover, the approach proposed in this article is also applicable to the global h-stability for multiple proportional delay high-order inertial neural networks. Finally, three examples and their numerical simulations are presented to illustrate the effectiveness of the method.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:407:y:2021:i:c:s0096300321003970
    DOI: 10.1016/j.amc.2021.126308
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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.
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    4. Xiong, Jun & Chang, Xiao-Heng & Yi, Xiaojian, 2018. "Design of robust nonfragile fault detection filter for uncertain dynamic systems with quantization," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 774-788.
    5. Dong, Zeyu & Wang, Xin & Zhang, Xian, 2020. "A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen–Grossberg neural networks," Applied Mathematics and Computation, Elsevier, vol. 385(C).
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    Cited by:

    1. Meng, Xianhe & Zhang, Xian & Wang, Yantao, 2023. "Bounded real lemmas and exponential H∞ control for memristor-based neural networks with unbounded time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 210(C), pages 66-81.
    2. Zihan Zou & Yinfang Song & Chi Zhao, 2022. "Razumikhin Theorems on Polynomial Stability of Neutral Stochastic Pantograph Differential Equations with Markovian Switching," Mathematics, MDPI, vol. 10(17), pages 1-15, August.
    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. Chen, Yonghui & Xue, Yu & Yang, Xiaona & Zhang, Xian, 2023. "A direct analysis method to Lagrangian global exponential stability for quaternion memristive neural networks with mixed delays," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    5. Zhang, Zhongjie & Yu, Tingting & Zhang, Xian, 2022. "Algebra criteria for global exponential stability of multiple time-varying delay Cohen–Grossberg neural networks," Applied Mathematics and Computation, Elsevier, vol. 435(C).

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