Improved exponential stability criterion for neural networks with time varying delay

被引：27
|
作者：
Liu, Xiaofan [1 ,2 ]
Liu, Xinge [1 ]
Tang, Meilan [1 ]
Wang, Fengxian [1 ,2 ]
机构：
[1] Cent S Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China
[2] Cent S Univ, Appl Math, Changsha 410083, Hunan, Peoples R China
来源：
NEUROCOMPUTING | 2017年 / 234卷
基金：
中国国家自然科学基金;
关键词：
Neural networks; Time-varying delay; Integral inequality; Exponential stability; GLOBAL ASYMPTOTIC STABILITY; PARTITIONING APPROACH; DISTRIBUTED DELAYS; DISCRETE; SYSTEMS;
D O I：
10.1016/j.neucom.2016.12.057
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the exponential stability for a class of neural networks with time-varying delay is concerned. An improved integral inequality is derived which extends the auxiliary function-based integral inequality. A novel Lyapounov-Krasovskii functional (LKF) with some new integral terms is constructed. Based on the improved integral inequality and reciprocally convex combination approach, a less conservative exponential stability criterion for the neural networks with time-varying delay is obtained. The effectiveness of the proposed method in this paper is illustrated via numerical examples.
引用
收藏
页码：154 / 163
页数：10
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