New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay

被引：2
|
作者：
Liu, Zixin [1 ,2 ]
Lv, Shu [1 ]
Zhong, Shouming [1 ]
Ye, Mao [3 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Appl Math, Chengdu 610054, Peoples R China
[2] Guizhou Coll Finance & Econ, Sch Math & Stat, Guiyang 550004, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Engn & Comp Sci, Chengdu 610054, Peoples R China
来源：
DISCRETE DYNAMICS IN NATURE AND SOCIETY | 2009年 / 2009卷
关键词：
GLOBAL ROBUST STABILITY; PERIODIC-SOLUTION; SYSTEMS;
D O I：
10.1155/2009/874582
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulated in the forms of linear matrix inequalities (LMIs). Compared with some previous results, the new results are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method. Copyright (C) 2009 Zixin Liu et al.
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页数：23
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