Global Lagrange stability for inertial neural networks with mixed time varying delays

被引：82
|
作者：
Wang, Jingfeng [1 ,2 ]
Tian, Lixin [1 ,3 ]
机构：
[1] Jiangsu Univ, Fac Sci, Zhenjiang 212013, Peoples R China
[2] Huaiyin Normal Univ, Sch Math Sci, Huaian 22300, Peoples R China
[3] Nanjing Normal Univ, Sch Math Sci, Nanjing 210046, Jiangsu, Peoples R China
来源：
NEUROCOMPUTING | 2017年 / 235卷
基金：
中国国家自然科学基金;
关键词：
Inertial neural networks; Lagrange stability; Time-varying delay; Global exponential attractive; EXPONENTIAL STABILITY; NEUTRAL-TYPE; SYNCHRONIZATION; SENSE; DISCRETE;
D O I：
10.1016/j.neucom.2017.01.007
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper concerns with the global Lagrange stability of inertial neural networks with discrete and distributed time-varying delays. By choosing a proper variable substitution, the inertial neural networks can be rewritten as a first-order differential system. Based on the Lyapunov functional method, inequality techniques and analytical method, several sufficient conditions are derived to guarantee the global exponential stability of the inertial neural networks in Lagrange sense. Meanwhile, the global exponential attractive set is also given. Simulation results demonstrate the effectiveness of the theoretical results.
引用
收藏
页码：140 / 146
页数：7
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