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Spacial sampled-data control for [formula omitted] output synchronization of directed coupled reaction–diffusion neural networks with mixed delays.

Authors :
Lu, Binglong
Jiang, Haijun
Hu, Cheng
Abdurahman, Abdujelil
Source :
Neural Networks. Mar2020, Vol. 123, p429-440. 12p.
Publication Year :
2020

Abstract

This work investigates the H ∞ output synchronization (HOS) of the directed coupled reaction–diffusion (R–D) neural networks (NNs) with mixed delays. Firstly, a model of the directed state coupled R–D NNs is introduced, which not only contains some discrete and distributed time delays, but also obeys a mixed Dirichlet–Neumann boundary condition. Secondly, a spacial sampled-data controller is proposed to achieve the HOS of the considered networks. This type of controller can reduce the update rate in the process of control by measuring the state of networks at some fixed sampling points in the space region. Moreover, some criteria for the HOS are established by designing an appropriate Lyapunov functional, and some quantitative relations between diffusion coefficients, mixed delays, coupling strength and control parameters are given accurately by these criteria. Thirdly, the case of directed spatial diffusion coupled networks is also studied and, the following finding is obtained: the spatial diffusion coupling can suppress the HOS while the state coupling can promote it. Finally, one example is simulated as the verification of the theoretical results. • H ∞ output synchronization for the directed coupled R–D NNs is defined. • A spacial sampled-data controller is proposed to lower spatial update rate. • Obtained conditions depend on diffusion, delay, and control parameters. • Both the directed state and spatial diffusion coupled R–D NNs are considered. • Synchronization can be suppressed by diffusion and promoted by state couple. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08936080
Volume :
123
Database :
Academic Search Index
Journal :
Neural Networks
Publication Type :
Academic Journal
Accession number :
141780596
Full Text :
https://doi.org/10.1016/j.neunet.2019.12.026