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An Improved Result on H∞ State Estimation for Static Neural Networks with Time-Varying Delay

被引：0
|
作者：
Jin Li
He Yong [1 ]
Zhang Chuan-Ke
Wu Min
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Hubei, Peoples R China
来源：
PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年
基金：
中国国家自然科学基金;
关键词：
Static neural networks; Time-varying delay; H-infinity state estimation; Lyapunov-Krasovskii functional; Relaxed integral inequality; Linear matrix inequality; STABILITY ANALYSIS; SYSTEMS; CRITERION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an improved result on H-infinity state estimation for static neural networks with a time-varying delay. First, a novel Lyapunov-Krasovskii functional (LKF) with several augmented terms is constructed. Then, a relaxed integral inequality is employed to make a tight estimation for single integral terms with time-varying delay in the derivative of the LKF. As a result, a delay-dependent criterion in terms of linear matrix inequalities is established. Finally, two numerical examples are given to demonstrate the benefits and effectiveness of the obtained criterion.
引用
收藏
页码：3948 / 3953
页数：6
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