Title
Mean-Square Asymptotic Synchronization Control of Discrete-Time Neural Networks With Restricted Disturbances and Missing Data.
Abstract
The problem of controller design is investigated to achieve the mean-square asymptotic synchronization of discrete-time neural networks with time-varying delay and restricted disturbances. The unreliable communication links between the neural networks, which are modeled as stochastic dropouts satisfying the Bernoulli distributions, are taken into account. By applying the Lyapunov function, a synchronization controller design method is proposed in the form of linear matrix inequalities. The design method is also extended to neural networks including modeling uncertainties. Two numerical examples are given to illustrate the effectiveness of the proposed method.
Year
DOI
Venue
2018
10.1109/ACCESS.2017.2779159
IEEE ACCESS
Keywords
Field
DocType
Asymptotic synchronization,controller design,discrete-time neural networks,time-varying delay,disturbance constraints,uncertainty,missing data
Lyapunov function,Synchronization,Computer science,Control theory,Matrix (mathematics),Stochastic process,Symmetric matrix,Missing data,Artificial neural network,Distributed computing,Bernoulli's principle
Journal
Volume
ISSN
Citations 
6
2169-3536
1
PageRank 
References 
Authors
0.35
0
4
Name
Order
Citations
PageRank
De-Hui Lin110.69
Jun Wu27216.70
Jian-Ping Cai310.69
Jian-Ning Li4444.74