Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays

被引：0
|
作者：
R. Saravanakumar
Grienggrai Rajchakit
M. Syed Ali
Zhengrong Xiang
Young Hoon Joo
机构：
[1] Maejo University,Department of Mathematics, Faculty of Science
[2] Thiruvalluvar University,Department of Mathematics
[3] Nanjing University of Science and Technology,School of Automation
[4] Kunsan National University,Department of Control and Robotics Engineering
[5] King Mongkut’s University of Technology Thonburi (KMUTT),Department of Mathematics, Faculty of Science
来源：
Neural Computing and Applications | 2018年 / 30卷
关键词：
Extended dissipativity analysis; Uncertain discrete-time neural networks; Lyapunov method; Linear matrix inequality;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this draft, we consider the problem of robust extended dissipativity for uncertain discrete-time neural networks (DNNs) with time-varying delays. By constructing appropriate Lyapunov–Krasovskii functional (LKF), sufficient conditions are established to ensure that the considered time-delayed uncertain DNN is extended dissipative. The derived conditions are presented in terms of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the superiority of this result.
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页码：3893 / 3904
页数：11
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