EXPONENTIAL STABILITY FOR TIME-DELAY NEURAL NETWORKS VIA NEW WEIGHTED INTEGRAL INEQUALITIES

被引：0
|
作者：
Shi, Chenyang [1 ]
Hoi, Kachon [2 ]
Vong, Seakweng [2 ]
机构：
[1] South China Agr Univ, Dept Math, Guangzhou, Peoples R China
[2] Univ Macau, Dept Math, Ave Univ, Macau, Peoples R China
来源：
NUMERICAL ALGEBRA CONTROL AND OPTIMIZATION | 2025年 / 15卷 / 02期
关键词：
Neural networks; exponential stability; Lyapunov-Krasovskii function- al; time-varying delay; CRITERIA; SYSTEMS; SYNCHRONIZATION;
D O I：
10.3934/naco.2023017
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We study exponential stability for a kind of neural networks having time-varying delay. By extending the auxiliary function-based integral inequality, a novel integral inequality is derived by using weighted orthogonal functions. Then, the new inequality is applied to investigate the exponential stability of time-delay neural networks via Lyapunov-Krasovskii functional (LKF) method. Numerical examples are given to verify the advantages of the proposed criterion.
引用
收藏
页码：341 / 356
页数：16
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