Finite-time stability of neutral-type neural networks with random time-varying delays

被引：9

作者：

Ali, M. Syed ^{[1
]}

Saravanan, S. ^{[1
]}

Zhu, Quanxin ^{[2
,3
,4
]}

机构：

[1] Thiruvalluvar Univ, Dept Math, Vellore, Tamil Nadu, India

[2] Nanjing Normal Univ, Sch Math Sci, Nanjing, Jiangsu, Peoples R China

[3] Nanjing Normal Univ, Inst Finance & Stat, Nanjing, Jiangsu, Peoples R China

[4] Univ Bielefeld, Dept Math, Bielefeld, Germany

来源：

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE | 2017年 / 48卷 / 15期

基金：

中国国家自然科学基金;

关键词：

Finite-time stability; linear matrix inequalities; Lyapunov-Krasovskii functional; neural networks; time-varying delays; H-INFINITY CONTROL; DEPENDENT STABILITY; DISTRIBUTED DELAYS; OUTPUT-FEEDBACK; EXPONENTIAL STABILITY; INTEGRAL INEQUALITY; JUMP SYSTEMS; DISCRETE; BOUNDEDNESS; CRITERIA;

D O I：

10.1080/00207721.2017.1367434

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 is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov-Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.

引用

页码：3279 / 3295

页数：17

共 60 条

[1] Robust finite-time H∞ control for a class of uncertain switched neural networks of neutral-type with distributed time varying delays [J].

Ali, M. Syed ;

Saravanan, S. .

NEUROCOMPUTING, 2016, 177 :454-468

[2] Novel H∞ state estimation of static neural networks with interval time-varying delays via augmented Lyapunov-Krasovskii functional [J].

Ali, M. Syed ;

Saravanakumar, R. ;

Arik, Sabri .

NEUROCOMPUTING, 2016, 171 :949-954

[3] New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays [J].

Ali, M. Syed ;

Saravanakumar, R. ;

Cao, Jinde .

NEUROCOMPUTING, 2016, 171 :1533-1547

[4] Stability of Markovian jumping recurrent neural networks with discrete and distributed time-varying delays [J].

Ali, M. Syed .

NEUROCOMPUTING, 2015, 149 :1280-1285

[5] Stability analysis of uncertain fuzzy Hopfield neural networks with time delays [J].

Ali, M. Syed ;

Balasubramaniam, P. .

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2009, 14 (06) :2776-2783

[6] Finite-time stabilization via dynamic output feedback [J].

Amato, F ;

Ariola, M ;

Cosentino, C .

AUTOMATICA, 2006, 42 (02) :337-342

[7] Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction [J].

Arefi, Mohammad M. ;

Zarei, Jafar ;

Karimi, Hamid R. .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2014, 351 (08) :4302-4316

[8] Finite-time stability analysis of discrete-time fuzzy Hopfield neural network [J].

Bai, Jianjun ;

Lu, Renquan ;

Xue, Anke ;

She, Qingshan ;

Shi, Zhonghua .

NEUROCOMPUTING, 2015, 159 :263-267

[9]

Boyd S., 1994, SIAM STUDIES APPL MA

[10] An Asynchronous Operation Approach to Event-Triggered Control for Fuzzy Markovian Jump Systems With General Switching Policies [J].

Cheng, Jun ;

Park, Ju H. ;

Zhang, Lixian ;

Zhu, Yanzheng .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (01) :6-18

← 1 2 3 4 5 6 →