Stability analysis of complex-valued neural networks with probabilistic time-varying delays

被引：116
|
作者：
Song, Qiankun [1 ]
Zhao, Zhenjiang [2 ]
Liu, Yurong [3 ,4 ]
机构：
[1] Chongqing Jiaotong Univ, Dept Math, Chongqing 400074, Peoples R China
[2] Huzhou Teachers Coll, Dept Math, Huzhou 313000, Peoples R China
[3] Yangzhou Univ, Dept Math, Yangzhou 225002, Jiangsu, Peoples R China
[4] King Abdulaziz Univ, Fac Engn, Commun Syst & Networks CSN Res Grp, Jeddah 21589, Saudi Arabia
来源：
NEUROCOMPUTING | 2015年 / 159卷
基金：
中国国家自然科学基金;
关键词：
Complex-valued neural networks; Probabilistic time-varying delays; Stability; Complex-valued linear matrix inequality; EXPONENTIAL STABILITY; ASSOCIATIVE MEMORY; GLOBAL STABILITY; STOCHASTIC DELAY; CRITERIA; SYNCHRONIZATION; NONLINEARITIES; PARAMETERS; DYNAMICS;
D O I：
10.1016/j.neucom.2015.02.015
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the stability of complex-valued neural networks with probabilistic time-varying delays is investigated. Two important integral inequalities that include Jensen's inequality as a special case are developed. By constructing proper Lyapunov-Krasovskii functional and employing inequality technique, several delay-distribution-dependent sufficient conditions are obtained to guarantee the global asymptotic and exponential stability of the addressed neural networks. These conditions are expressed in terms of complex-valued LMIs, which can be checked numerically using the effective YALMIP toolbox in MATLAB. An example with simulations is given to show the effectiveness of the obtained results. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：96 / 104
页数：9
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