Paper Info
Title
Stability and Synchronization of Nonautonomous Reaction–Diffusion Neural Networks With General Time-Varying Delays
Abstract
This article investigates the stability and synchronization of nonautonomous reaction–diffusion neural networks with general time-varying delays. Compared with the existing works concerning reaction–diffusion neural networks, the main innovation of this article is that the network coefficients are time-varying, and the delays are general (which means that fewer constraints are posed on delays; for example, the commonly used conditions of differentiability and boundedness are no longer needed). By Green’s formula and some analytical techniques, some easily checkable criteria on stability and synchronization for the underlying neural networks are established. These obtained results not only improve some existing ones but also contain some novel results that have not yet been reported. The effectiveness and superiorities of the established criteria are verified by three numerical examples.
Year
DOI
Venue
2022
10.1109/TNNLS.2021.3071404
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Asymptotic stability,asymptotic synchronization,reaction–diffusion neural networks,time-varying delay
Journal
33
Issue
ISSN
Citations 
10
2162-237X
0
PageRank 
References 
Authors
0.34
43
2
Name
Order
Citations
PageRank
Hao Zhang120758.59
Zhigang Zeng23962234.23