Exponential synchronization for stochastic neural networks driven by fractional Brownian motion

被引：13

作者：

Zhou, Wuneng ^{[1
,2
]}

Zhou, Xianghui ^{[1
,3
]}

Yang, Jun ^{[4
]}

Liu, Yanjun ^{[1
]}

Zhang, Xiaolu ^{[1
]}

Ding, Xiangwu ^{[5
]}

机构：

[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 200051, Peoples R China

[2] Donghua Univ, Minist Educ, Engn Res Ctr Digitized Text & Fash Technol, Shanghai 201620, Peoples R China

[3] Yangtze Univ, Sch Informat & Math, Jing Zhou 434020, Peoples R China

[4] Anyang Normal Univ, Sch Math & Stat, Anyang 455000, Peoples R China

[5] Donghua Univ, Coll Comp Sci & Technol, Shanghai 200051, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2016年 / 353卷 / 08期

基金：

上海市自然科学基金;

关键词：

DIFFERENTIAL-EQUATIONS DRIVEN; ADAPTIVE SYNCHRONIZATION; TIME-DELAY; EXISTENCE; STABILITY; SPACE;

D O I：

10.1016/j.jfranklin.2016.02.019

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we investigate the exponential synchronization problem for a new class of stochastic neural networks driven by fractional Brownian motion (fBm). In order to deal with the problem of stability for the error system, we propose the mild solution of system equation with respect to fractional Brownian motion based on the space of Hilbert Schmidt operator law. By using the infinitesimal generator on analytic semigroup principle and associating with the well-known Holder inequality, Gronwall inequality, we obtain the exponential synchronization criteria for the drive system and response system driven by fBm. Finally, two numerical examples as well as some evolution figures are implemented to demonstrate the effectiveness and feasibility of the proposed exponential synchronization schemes. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：1689 / 1712

页数：24

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