A NEW STABILITY CRITERION OF NEUTRAL NEURAL NETWORKS WITH TIME-VARYING DELAYS

被引：0
|
作者：
Luo, Ricai [1 ]
Xu, Honglei [2 ]
Wang, Wusheng [3 ]
Wang, Xiangyu [4 ]
机构：
[1] Hechi Univ, Dept Comp & Informat Sci, Yizhou 546300, Guangxi, Peoples R China
[2] Curtin Univ, Dept Math & Stat, Perth, WA 6845, Australia
[3] Hechi Univ, Sch Math & Stat, Yizhou 546300, Guangxi, Peoples R China
[4] Curtin Univ, Sch Built Environm, Perth, WA 6845, Australia
来源：
PACIFIC JOURNAL OF OPTIMIZATION | 2016年 / 12卷 / 03期
关键词：
neutral neural networks; time-varying delays; global asymptotic stability; sufficient conditions; COMBINATORIAL OPTIMIZATION PROBLEMS;
D O I：
暂无
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
In this paper, we study the global stability problem for neutral neural networks with time delays. Firstly, we extend the existing neutral networks to a much general class of such networks. Then, by constructing suitable Lyapunov-Krasovskii-type functionals and using linear matrix inequality (LMI) optimization techniques, we obtain new sufficient conditions for global asymptotic stability of the neural networks. The obtained results are related to some positive real-value parameters rather than positive symmetric matrices which are much complicated computationally. Finally, we demonstrate the results' validity via a numerical example and its simulations.
引用
收藏
页码：487 / 496
页数：10
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