GLOBAL STABILITY ANALYSIS FOR A CLASS OF COHEN-GROSSBERG NEURAL NETWORK MODELS

被引：29
作者：
Guo, Yingxin [1 ,2 ]
机构：
[1] Shandong Univ, Coll Control Sci & Engn, Jinan 250061, Shandong, Peoples R China
[2] Qufu Normal Univ, Sch Math Sci, Qufu 273165, Shandong, Peoples R China
来源：
BULLETIN OF THE KOREAN MATHEMATICAL SOCIETY | 2012年 / 49卷 / 06期
基金：
中国国家自然科学基金;
关键词：
delay differential equations; Lyapunov functionals; matrix inequality; global asymptotic stability; EXPONENTIAL STABILITY; DELAYS; DYNAMICS; MEMORY;
D O I：
10.4134/BKMS.2012.49.6.1193
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
By constructing suitable Lyapunov functionals and combining with matrix inequality technique, a new simple sufficient condition is presented for the global asymptotic stability of the Cohen-Grossberg neural network models. The condition contains and improves some of the previous results in the earlier references.
引用
收藏
页码：1193 / 1198
页数：6
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