DocumentCode
1307559
Title
Adaptive Lag Synchronization for Competitive Neural Networks With Mixed Delays and Uncertain Hybrid Perturbations
Author
Yang, Xinsong ; Cao, Jinde ; Long, Yao ; Rui, Weiguo
Author_Institution
Dept. of Math., Honghe Univ., Mengzi, China
Volume
21
Issue
10
fYear
2010
Firstpage
1656
Lastpage
1667
Abstract
This paper investigates the problem of adaptive lag synchronization for a kind of competitive neural network with discrete and distributed delays (mixed delays), as well as uncertain nonlinear external and stochastic perturbations (hybrid perturbations). A simple but robust adaptive controller is designed such that the response system can lag-synchronize with a drive system. Based on the Lyapunov stability theory and some suitable Lyapunov-Krasovskii functionals, several sufficient conditions ensuring the lag synchronization are developed. Our synchronization criteria are easily verified and do not need to solve any linear matrix inequality. Some existing results are improved and extended. Moreover, the designed adaptive controller has better anti-interference capacity and is more practical than the usual adaptive controller. Numerical simulations are exploited to show the effectiveness of the theoretical results.
Keywords
Lyapunov methods; adaptive control; delays; linear matrix inequalities; neural nets; neurocontrollers; perturbation techniques; robust control; synchronisation; uncertain systems; Lyapunov stability theory; Lyapunov-Krasovskii functionals; adaptive lag synchronization; anti-interference capacity; competitive neural networks; discrete delays; distributed delays; linear matrix inequality; mixed delays; robust adaptive controller; stochastic perturbations; uncertain hybrid perturbations; Adaptive systems; Artificial neural networks; Chaotic communication; Delay; Neurons; Synchronization; Competitive neural networks; lag synchronization; mixed delay; nonlinear perturbations; time scale; vector-form noise; Algorithms; Computer Simulation; Neural Networks (Computer); Nonlinear Dynamics; Stochastic Processes; Time Factors;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/TNN.2010.2068560
Filename
5559474
Link To Document