Paper Info
Title
Impulsive synchronization of two coupled delayed reaction-diffusion neural networks using time-varying impulsive gains
Abstract
In this paper, the problem of impulsive synchronization of two coupled delayed reaction–diffusion neural networks under aperiodic discrete measurements is revisited. Different from the previous static impulsive gain based impulsive synchronization strategy, a novel impulsive synchronization strategy using sampling-interval-dependent impulsive gains is proposed. The time-varying impulsive synchronization gains are able to adapt to the variation of sampling intervals, and thus can improve the synchronization performance. The stability analysis of the resultant synchronization error system is performed by applying an impulse-time-dependent discretized Lyapunov functions based method. Sufficient conditions for the existence of desired impulsive synchronization controllers are derived in terms of a set of linear matrix inequalities (LMIs). These conditions allow to synthesize time-varying impulsive gains. A numerical example is presented to demonstrate the effectiveness of the developed methodology.
Year
DOI
Venue
2020
10.1016/j.neucom.2019.08.098
Neurocomputing
Keywords
Field
DocType
Impulsive synchronization,Reaction–diffusion neural networks,Time-varying impulsive gains,Impulse-time-dependent discretized Lyapunov functions
Lyapunov function,Discretization,Synchronization,Pattern recognition,Matrix (mathematics),Control theory,Artificial intelligence,Sampling (statistics),Artificial neural network,Aperiodic graph,Reaction–diffusion system,Mathematics
Journal
Volume
ISSN
Citations 
377
0925-2312
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Wu-Hua Chen186958.24
Xiaoqing Deng200.34
Xiaomei Lu31248.38