Discrete-time BAM neural networks with variable delays

被引：44
|
作者：
Liu, Xin-Ge [1 ]
Tang, Mei-Lan
Martin, Ralph
Liu, Xin-Bi
机构：
[1] Cent S Univ, Sch Math Sci & Comp Technol, Changsha 410083, Hunan, Peoples R China
[2] Cardiff Univ, Sch Comp Sci, Cardiff, Wales
[3] Cent S Univ, Sch Mat Sci & Engn, Changsha 410083, Hunan, Peoples R China
[4] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
来源：
PHYSICS LETTERS A | 2007年 / 367卷 / 4-5期
关键词：
BAM neural network; discrete-time; global exponential stability; variable delay; linear matrix inequality (LMI); delay-dependent;
D O I：
10.1016/j.physleta.2007.03.037
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development. (c) 2007 Elsevier BX All rights reserved.
引用
收藏
页码：322 / 330
页数：9
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