Almost surely asymptotic synchronization for stochastic neural networks of neutral type with Markovian jumping parameters

被引：8

作者：

Wu, Tao ^{[1
]}

Xiong, Lianglin ^{[1
,2
]}

Cao, Jinde ^{[2
]}

Xie, Xueqin ^{[1
]}

机构：

[1] Yunnan Minzu Univ, Sch Math & Comp Sci, Kunming 650500, Yunnan, Peoples R China

[2] Southeast Univ, Sch Math, Nanjing 210096, Jiangsu, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2019年 / 33卷 / 10期

基金：

中国国家自然科学基金;

关键词：

almost surely asymptotic synchronization; LaSalle invariant principle; Markovian jumping parameters; state-feedback control; stochastic neural network; time-varying delay; TIME-VARYING DELAYS; ADAPTIVE SYNCHRONIZATION; EXPONENTIAL STABILITY; LEVY NOISE; FAULT-DETECTION; SYSTEMS; DISCRETE; CRITERION; EQUATIONS; DESIGN;

D O I：

10.1002/acs.3047

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper studies the problem of the almost surely asymptotic synchronization for a class of stochastic neural networks of neutral type with both Markovian jumping parameters and mixed time delays. Based on the stochastic analysis theory, LaSalle-type invariance principle, and delayed state-feedback control technique, some novel delay-dependent sufficient criteria to guarantee the almost surely asymptotic synchronization are given. These criteria are expressed as the linear matrix inequalities, which can be easily checked by MATLAB LMI Control Toolbox. Finally, four numerical examples and their simulations are provided to illustrate the effectiveness of the proposed method.

引用

页码：1524 / 1551

页数：28

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