Exponential stability for delayed complex-valued neural networks with reaction-diffusion terms

被引：0
|
作者：
Xiaohui Xu
Jibin Yang
Quan Xu
Yanhai Xu
Shulei Sun
机构：
[1] Xihua University,Key Laboratory of Fluid and Power Machinery, Ministry of Education
[2] Xihua University,Key Laboratory of Automobile Measurement and Control & Safty, School of Automobile & Transportation
[3] Xihua University,School of Mechanical Engineering
来源：
Advances in Difference Equations | / 2021卷
关键词：
Complex-valued neural networks; Reaction-diffusion terms; Time-varying delays; Infinite distributed delays; Exponential stability; Vector Lyapunov function method;
D O I：
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摘要：
In this study, we investigate reaction-diffusion complex-valued neural networks with mixed delays. The mixed delays include both time-varying and infinite distributed delays. Criteria are derived to ensure the existence, uniqueness, and exponential stability of the equilibrium state of the addressed system on the basis of the M-matrix properties and homeomorphism mapping theories as well as the vector Lyapunov function method. The results demonstrate the positive effect of reaction-diffusion on the stability, which further improves the existing conditions. Finally, the analysis of several examples is compared to the present results to verify the correctness and reduced conservatism of the primary results.
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