Global asymptotic stability of complex-valued neural networks with additive time-varying delays

被引：35
|
作者：
Subramanian, K. [1 ]
Muthukumar, P. [1 ]
机构：
[1] Deemed Univ, Gandhigram Rural Inst, Dept Math, Gandhigram 624302, Tamil Nadu, India
来源：
COGNITIVE NEURODYNAMICS | 2017年 / 11卷 / 03期
关键词：
Additive time-varying delays; Complex-valued neural networks; Global asymptotic stability; Leakage delay; Lyapunov-Krasovskii functional; EXPONENTIAL STABILITY; CONTINUOUS SYSTEM; LEAKAGE DELAYS; CRITERIA; SYNCHRONIZATION; PASSIVITY;
D O I：
10.1007/s11571-017-9429-1
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
In this paper, we extensively study the global asymptotic stability problem of complex-valued neural networks with leakage delay and additive time-varying delays. By constructing a suitable Lyapunov-Krasovskii functional and applying newly developed complex valued integral inequalities, sufficient conditions for the global asymptotic stability of proposed neural networks are established in the form of complex-valued linear matrix inequalities. This linear matrix inequalities are efficiently solved by using standard available numerical packages. Finally, three numerical examples are given to demonstrate the effectiveness of the theoretical results.
引用
收藏
页码：293 / 306
页数：14
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