A new stability criterion for bidirectional associative memory neural networks of neutral-type

被引：171
|
作者：
Park, Ju H. [1 ]
Park, C. H. [2 ]
Kwon, O. M. [3 ]
Lee, S. M. [4 ]
机构：
[1] Yeungnam Univ, Dept Elect Engn, Robust Control & Nonlinear Dynam Lab, Kyongsan 712749, South Korea
[2] Yeungnam Univ, Dept Comp Engn, Kyongsan 712749, South Korea
[3] Chungbuk Natl Univ, Sch Elect & Comp Engn, Cheongju, South Korea
[4] KT Co Ltd, BcN Business Unit, Platform Verificat Div, Taejon, South Korea
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2008年 / 199卷 / 02期
关键词：
global stability; BAM neural network; delay; linear matrix inequality; Lyapunov method;
D O I：
10.1016/j.amc.2007.10.032
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In the paper, the global asymptotic stability of equilibrium is considered for continuous bidirectional associative memory ( BAM) neural networks of neutral type by using the Lyapunov method. A new stability criterion is derived in terms of linear matrix inequality ( LMI) to ascertain the global asymptotic stability of the BAM. The LMI can be solved easily by various convex optimization algorithms. A numerical example is illustrated to verify our result. (C) 2007 Elsevier Inc. All rights reserved.
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页码：716 / 722
页数：7
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