Improved delay-dependent stability criteria for generalized neural networks with time-varying delays

被引：35
|
作者：
Yang, Bin [1 ]
Wang, Juan [1 ]
Liu, Xiaodong [1 ]
机构：
[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China
来源：
INFORMATION SCIENCES | 2017年 / 420卷
关键词：
Generalized neural networks; Time-varying delays; Lyapunov-Krasovskii functional; Integral inequality; GLOBAL ASYMPTOTIC STABILITY; PARTITIONING METHOD; DISTRIBUTED DELAY; SYSTEMS; INEQUALITY; DISCRETE;
D O I：
10.1016/j.ins.2017.08.072
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 is concerned with the problem of stability analysis for generalized neural networks with time-varying delays. A novel integral inequality which includes several existing inequalities as special cases is presented. By employing a suitable Lyapunov-Krasovskii functional (LKF) and using the proposed integral inequality to estimate the derivative of the LKF, improved delay-dependent stability criteria expressed in terms of linear matrix inequalities are derived. Finally, four numerical examples are provided to demonstrate the effectiveness and the improvement of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码：299 / 312
页数：14
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