Exponential stability analysis of neural networks with a time-varying delay via a generalized Lyapunov-Krasovskii functional method

被引:5
|
作者
Li, Xu [1 ]
Liu, Haibo [1 ]
Liu, Kuo [1 ]
Li, Te [1 ]
Wang, Yongqing [1 ]
机构
[1] Dalian Univ Technol, Key Lab Precis & Nontradit Machining Technol, Minist Educ, Dalian 116024, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
exponential stability analysis; generalized Lyapunov‐ Krasovskii functionals; neural networks; time‐ varying delays; GLOBAL ASYMPTOTIC STABILITY; LINEAR-SYSTEMS; STATE ESTIMATION; CRITERIA; STABILIZATION; INEQUALITY;
D O I
10.1002/rnc.5304
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As is known to all that the Lyapunov-Krasovskii functional (LKF) method plays a significant role in deriving exponential stability criteria of neural networks with a time-varying delay. However, when the LKF method is adopted, the condition that a functional is required for a neural network with a delay varying in a delay interval is so strong that it may be hard to be satisfied and lead to a conservative criterion. Therefore, a generalized LKF method is proposed by weakening the strong condition in this paper. Then, new exponential stability criteria are derived via applying the proposed method. Finally, the effectiveness of the derived criteria is verified by two numerical examples.
引用
收藏
页码:716 / 732
页数:17
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