Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation

被引：131
|
作者：
Selvaraj, P. [1 ]
Sakthivel, R. [2 ,3 ]
Kwon, O. M. [1 ]
机构：
[1] Chungbuk Natl Univ, Sch Elect Engn, 1 Chungdao Ro, Cheongju 28644, South Korea
[2] Bharathiar Univ, Dept Math, Coimbatore 641046, Tamil Nadu, India
[3] Sungkyunkwan Univ, Dept Math, Suwon 440746, South Korea
来源：
NEURAL NETWORKS | 2018年 / 105卷
基金：
新加坡国家研究基金会;
关键词：
Coupled stochastic neural networks; Finite-time synchronization; Saturation effect; Markovian jumping parameters; Stochastic coupling strength; VARYING DELAY; MULTIAGENT SYSTEMS; DYNAMICAL NETWORKS; NONLINEAR-SYSTEMS; STABILIZATION; STABILITY; DESIGN; PARAMETERS; CONSENSUS;
D O I：
10.1016/j.neunet.2018.05.004
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 addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizing a simple linear transformation, the problem of stochastic finite-time synchronization of SCNNs is converted into a mean-square finite-time stabilization problem of an error system. By choosing a suitable mode dependent switched Lyapunov-Krasovskii functional, a new set of sufficient conditions is derived to guarantee the finite-time stability of the error system. Subsequently, with the help of anti-windup control scheme, the actuator saturation risks could be mitigated. Moreover, the derived conditions help to optimize estimation of the domain of attraction by enlarging the contractively invariant set. Furthermore, simulations are conducted to exhibit the efficiency of proposed control scheme. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：154 / 165
页数：12
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