Stability Analysis of Anti-Periodic Solutions for Cohen-Grossberg Neural Networks with Inertial Term and Time Delays

被引：5

作者：

Cheng, Jiaxin ^{[1
]}

Liu, Weide ^{[2
]}

机构：

[1] Hechi Univ, Sch Math & Phys, Hechi 546300, Peoples R China

[2] Guangxi Minzu Univ, Ctr Appl Math Guangxi, Sch Math & Phys, Nanning 530006, Peoples R China

来源：

MATHEMATICS | 2024年 / 12卷 / 02期

关键词：

inertial term; cohen-grossberg neural networks (CGNNs); lyapunov functional; anti-periodic solutions; globally exponential stability; GLOBAL EXPONENTIAL STABILITY; SYNCHRONIZATION; PERIODICITY;

D O I：

10.3390/math12020198

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This work is dedicated to exploring the globally exponential stability of anti-periodic solutions in inertial CGNNs that incorporate time delays. This is based on a strategic variable substitution to transform the complex system into a first-order differential equation. By leveraging the Lyapunov functional and demonstrating uniformly converging properties, we establish sufficient conditions that guarantee the existence and global exponential stability of anti-periodic solutions for the system. Finally, examples are presented to illustrate the effectiveness of the obtained theoretical results. This work contributes significantly to enhancing our understanding of the stability dynamics in neural networks with time delays and provides valuable insights for applications across various fields.

引用

页数：18

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