Improved Delay-Dependent Stability Criterion on Neural Networks with Time-Varying Delay

被引：1
|
作者：
Zhang, Haitao [1 ]
Wang, Ting [1 ]
Fei, Shumin [1 ]
Li, Tao [2 ]
机构：
[1] Southeast Univ, Sch Automat, Minist Educ, Key Lab Measurement & Control CSE, Nanjing 210096, Peoples R China
[2] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo 454003, Henan, Peoples R China
来源：
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年
关键词：
Delayed neural networks (DNNs); asymptotical stability; Lyapunov-Krasovskii functional (LKF); time-varying delay; LMI technique; GLOBAL ASYMPTOTIC STABILITY; EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; DISCRETE; SYSTEMS;
D O I：
10.1109/WCICA.2010.5554390
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, based on Lyapunov-Krasovskii functional approach and proper integral inequality, one novel sufficient condition is derived to guarantee the global stability for neural networks with interval time-varying delay, in which the general convex combination is employed. The LMI-based criterion heavily depends on the upper and lower bounds on both time delay and its derivative, which is different from those existent ones and has wider application fields than some present results. Finally, two numerical examples can illustrate the less conservatism of the proposed methods.
引用
收藏
页码：2080 / 2084
页数：5
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