Synchronization of complex-valued neural networks with mixed two additive time-varying delays

被引：34
|
作者：
Yuan, Yuefei [1 ]
Song, Qiankun [1 ]
Liu, Yurong [2 ,3 ]
Alsaadi, Fuad E. [3 ]
机构：
[1] Chongqing Jiaotong Univ, Dept Math, Chongqing 400074, Peoples R China
[2] Yangzhou Univ, Dept Math, Yangzhou 225002, Jiangsu, Peoples R China
[3] King Abdulaziz Univ, Fac Engn, CSN Res Grp, Jeddah 21589, Saudi Arabia
来源：
NEUROCOMPUTING | 2019年 / 332卷
基金：
中国国家自然科学基金;
关键词：
Complex-valued neural networks; Additive time-varying delays; Synchronization; Complex-valued linear matrix inequality; DEPENDENT STABILITY-CRITERIA; ASYMPTOTIC STABILITY; DISCRETE; SYSTEMS; BOUNDEDNESS; FAILURES;
D O I：
10.1016/j.neucom.2018.12.023
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper focus on the synchronization of complex-valued neural networks (CVNNs) with both discrete and distributed two additive time-varying delays. By applying matrix inequality technique and exploiting reciprocally convex approach, several delay-dependent criteria are presented in the form of linear matrix inequalities (LMIs) to ensure the global synchronization of CVNNs via structuring an appropriate Lyapunov-Krasovskii functional. An example with simulations is provided to ensure the feasibility of the obtained result. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：149 / 158
页数：10
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