IMPROVED RESULTS ON STABILITY ANALYSIS OF NEURAL NETWORKS WITH TIME-VARYING DELAYS: NOVEL DELAY-DEPENDENT CRITERIA

被引：29
|
作者：
Kwon, O. M. [2 ]
Lee, S. M. [3 ]
Park, Ju H. [1 ]
机构：
[1] Yeungnam Univ, Dept Elect Engn, Kyongsan 712749, South Korea
[2] Chungbuk Natl Univ, Sch Elect Engn, Cheongju 361763, South Korea
[3] Daegu Univ, Dept Elect Engn, Gyongsan 712714, South Korea
来源：
MODERN PHYSICS LETTERS B | 2010年 / 24卷 / 08期
关键词：
Neural networks; time-varying delays; LMI; Lyapunov method; GLOBAL ASYMPTOTIC STABILITY; NEUTRAL-TYPE; SYNCHRONIZATION; SYSTEMS; MODEL;
D O I：
10.1142/S0217984910022858
中图分类号：
O59 [应用物理学];
学科分类号：
摘要：
In this paper, the problem of stability analysis of neural networks with discrete time-varying delays is considered. By constructing a new Lyapunov functional and some novel analysis techniques, new delay-dependent criteria for checking the asymptotic stability of the neural networks are established. The criteria are presented in terms of linear matrix inequalities, which can be easily solved and checked by various convex optimization algorithms. Three numerical examples are included to show the superiority of our results.
引用
收藏
页码：775 / 789
页数：15
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