Globally exponential stability of non-autonomous delayed neural networks

被引：0
|
作者：
Zhang, Q [1 ]
Liu, WB
Wei, XP
Xu, J
机构：
[1] Dalian Univ, Univ Key Lab Informat Sci & Engn, Dalian 116622, Peoples R China
[2] Wenzhou Normal Coll, Sch Engn & Comp Sci, Wenzhou 325027, Peoples R China
来源：
PATTERN RECOGNITION AND IMAGE ANALYSIS, PT 2, PROCEEDINGS | 2005年 / 3523卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Globally exponential stability of non-autonomous delayed neural networks is considered in this paper. By utilizing delay differential inequalities, a new sufficient condition ensuring globally exponential stability for non-autonomous delayed neural networks is presented. The condition does not require that the delay function be differentiable or the coefficients be bounded. Due to this reason, the condition improves and extends those given in the previous literature.
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页码：91 / 96
页数：6
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