Novel results on stability analysis of neutral-type neural networks with additive time-varying delay components and leakage delay

被引：0
作者：
R. Samidurai
S. Rajavel
R. Sriraman
Jinde Cao
Ahmed Alsaedi
Fuad E. Alsaadi
机构：
[1] Thiruvalluvar University,Department of Mathematics
[2] Southeast University,School of Mathematics, and Research Center for Complex Systems and Network Sciences
[3] Shandong Normal University,School of Mathematical Sciences
[4] King Abdulaziz University,Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics
[5] King Abdulaziz University,Department of Electrical and Computer Engineering, Faculty of Engineering
来源：
International Journal of Control, Automation and Systems | 2017年 / 15卷
关键词：
Additive time-varying delays; linear matrix inequality; Lyapunov-Krasovskii functional; neural networks; neutral-type;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The objective of this paper is to analyze the stability analysis of neutral-type neural networks with additive time-varying delay and leakage delay. By constructing a suitable augmented Lyapunov-Krasovskii functional with triple and four integral terms, some new stability criteria are established in terms of linear matrix inequalities, which is easily solved by various convex optimization techniques. More information of the lower and upper delay bounds of time-varying delays are used to derive the stability criteria, which can lead less conservative results. The obtained conditions are expressed with linear matrix inequalities (LMIs) whose feasible can be checked easily by MATLAB LMI control toolbox. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed method.
引用
收藏
页码：1888 / 1900
页数：12
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