Robust H a Performance of Discrete-time Neural Networks with Uncertainty and Time-varying Delay

被引：4
|
作者：
Ali, M. Syed [1 ]
Meenakshi, K. [1 ]
Vadivel, R. [1 ]
Kwon, O. M. [2 ]
机构：
[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India
[2] Chungbuk Natl Univ, Sch Elect Engn, Chungdae Ro 1, Cheongju 28644, South Korea
来源：
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2018年 / 16卷 / 04期
基金：
新加坡国家研究基金会;
关键词：
H-infinity control; linear matrix inequality; stability; time-varying delay; STABILITY ANALYSIS; EXPONENTIAL STABILITY; INFINITY CONTROL; FUZZY CONTROL; SYSTEMS; INEQUALITY; CRITERIA;
D O I：
10.1007/s12555-017-0416-4
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, we are concerned with the robust H (a) problem for a class of discrete-time neural networks with uncertainties. Under a weak assumption on the activation functional, some novel summation inequality techniques and using a new Lyapunov-Krasovskii (L-K) functional, a delay-dependent condition guaranteeing the robust asymptotically stability of the concerned neural networks is obtained in terms of a Linear Matrix Inequality(LMI). It is shown that this stability condition is less conservative than some previous ones in the literature. The controller gains can be derived by solving a set of LMIs. Finally, two numerical examples result are given to illustrate the effectiveness of the developed theoretical results.
引用
收藏
页码：1637 / 1647
页数：11
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