Improved Criteria on Delay-Dependent Stability for Discrete-Time Neural Networks with Interval Time-Varying Delays

被引：2
|
作者：
Kwon, O. M. [2 ]
Park, M. J. [2 ]
Park, Ju H. [1 ]
Lee, S. M. [3 ]
Cha, E. J. [4 ]
机构：
[1] Yeungnam Univ, Dept Elect Engn, Kyongsan 712749, South Korea
[2] Chungbuk Natl Univ, Sch Elect Engn, Cheongju 361763, South Korea
[3] Daegu Univ, Sch Elect Engn, Gyongsan 712714, South Korea
[4] Chungbuk Natl Univ, Sch Med, Dept Biomed Engn, Cheongju 361763, South Korea
来源：
ABSTRACT AND APPLIED ANALYSIS | 2012年
基金：
新加坡国家研究基金会;
关键词：
EXPONENTIAL STABILITY; ROBUST STABILITY; SYSTEMS;
D O I：
10.1155/2012/285931
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The purpose of this paper is to investigate the delay-dependent stability analysis for discrete-time neural networks with interval time-varying delays. Based on Lyapunov method, improved delay-dependent criteria for the stability of the networks are derived in terms of linear matrix inequalities (LMIs) by constructing a suitable Lyapunov-Krasovskii functional and utilizing reciprocally convex approach. Also, a new activation condition which has not been considered in the literature is proposed and utilized for derivation of stability criteria. Two numerical examples are given to illustrate the effectiveness of the proposed method.
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收藏
页数：16
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