Paper Info
Title
Asymptotic Synchronization Control of Discrete-Time Delayed Neural Networks With a Reuse Mechanism Under Missing Data and Uncertainty.
Abstract
This paper focuses on the mean-square asymptotic synchronization of discrete-time delayed neural networks with missing data and uncertainty. The unreliable communication links between neural networks are considered, and the process of missing data is modeled as a stochastic process that satisfies Bernoulli distribution. A delay-dependent criterion is given in the form of matrix inequalities using the Lyapunov function approach. Then, a feedback controller is designed based on a reuse mechanism, which avoids the fluctuation of the controller input compared with the existing literature to ensure that the master-slave system with uncertainties is asymptotically synchronized in mean square. Simulated annealing (SA) algorithm is used to obtain the controller. Finally, numerical examples are presented to illustrate the effectiveness of the theoretical result.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2870729
IEEE ACCESS
Keywords
Field
DocType
Asymptotic synchronization control,discrete-time neural network,time-varying delay,controller design,reuse mechanism,simulated annealing algorithm,uncertainty
Simulated annealing,Lyapunov function,Control theory,Synchronization,Computer science,Control theory,Stochastic process,Discrete time and continuous time,Missing data,Artificial neural network,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
De-Hui Lin110.69
Jun Wu245675.01
Jian-Ning Li3444.74
Jian-Ping Cai410.69