Improved delay-dependent stability conditions for recurrent neural networks with multiple time-varying delays

被引：12
|
作者：
Chen, Yonggang [1 ]
Fei, Shumin [2 ]
Li, Yongmin [3 ]
机构：
[1] Henan Inst Sci & Technol, Sch Math Sci, Xinxiang 453003, Peoples R China
[2] Southeast Univ, Sch Automat, Minist Educ, Key Lab Measurement & Control CSE, Nanjing 210096, Jiangsu, Peoples R China
[3] Huzhou Teachers Coll, Sch Sci, Huzhou 313000, Peoples R China
来源：
NONLINEAR DYNAMICS | 2014年 / 78卷 / 02期
关键词：
Delay-dependent stability; Recurrent neural networks; Multiple time-varying delays; GLOBAL ASYMPTOTIC STABILITY; ROBUST EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; DISCRETE DELAYS; LMI APPROACH; CRITERIA; SYSTEMS;
D O I：
10.1007/s11071-014-1478-y
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
This paper investigates the global asymptotic stability problem for recurrent neural networks with multiple time-varying delays. Using the free-weighting matrix technique, and incorporating the interconnected information between the upper bounds of multiple time-varying delays, two less conservative delay-dependent asymptotic stability conditions are proposed, which are expressed by linear matrix inequalities, and can be conveniently solved by the existing softwares. Numerical examples show the reduce conservatism of the obtained conditions.
引用
收藏
页码：803 / 812
页数：10
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