Enhanced stability criteria of neural networks with time-varying delays via a generalized free-weighting matrix integral inequality

被引：52
作者：
Park, M. J. [1 ]
Lee, S. H. [2 ]
Kwon, O. M. [2 ]
Ryu, J. H. [3 ]
机构：
[1] Kyung Hee Univ, Ctr Global Converging Humanities, 1732 Deogyeong Daero, Yongin 17104, South Korea
[2] Chungbuk Natl Univ, Sch Elect Engn, 1 Chungdae Ro, Cheongju 28644, South Korea
[3] Elect & Telecommun Res Inst, 176-11 Cheomdan Gwagi Ro, Gwangju 61012, South Korea
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2018年 / 355卷 / 14期
基金：
新加坡国家研究基金会;
关键词：
LINEAR-SYSTEMS; DISCRETE;
D O I：
10.1016/j.jfranklin.2018.06.023
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 deals with the problem of delay-dependent stability analysis for neural networks with time-varying delays. First, by constructing an augmented Lyapunov-Krasovskii functional and utilizing a generalized free-weighting matrix integral inequality, an improved stability criterion for the concerned network is derived in terms of linear matrix inequalities. Second, by considering a marginal augmented vector and modifying a Lyapunov-Krasovsii functional, a further enhanced stability criterion is presented. Third, a less conservative stability condition in which a relaxed inequality related to activation functions is added is introduced. Finally, three numerical examples are included to illustrate the advantage and validity of the proposed criteria. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：6531 / 6548
页数：18
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