English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Stabilization of synchronous equilibria in regular dynamical networks with delayed coupling

Maia, D., Kurths, J., Yanchuk, S. (2023): Stabilization of synchronous equilibria in regular dynamical networks with delayed coupling. - Nonlinear Dynamics, 111, 7377-7390.
https://doi.org/10.1007/s11071-022-08220-w

Item is

Files

show Files
hide Files
:
Maia_2023_s11071-022-08220-w.pdf (Publisher version), 922KB
 
File Permalink:
-
Name:
Maia_2023_s11071-022-08220-w.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Maia, Daniel1, Author
Kurths, Jürgen2, Author              
Yanchuk, Serhiy2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: We consider the synchronization problem of dynamical networks with delayed interactions. More specifically, we focus on the stabilization of synchronous equilibria in regular networks where the degrees of all nodes are equal. By studying such control near a Hopf bifurcation, we obtain necessary and sufficient conditions for stabilization. It is shown that the stabilization domains in the parameter space reappear periodically with time-delay. We find that the frequency of reappearance of the control domains is linearly proportional to the number of cycle multipartitions of the network.

Details

show
hide
Language(s): eng - English
 Dates: 2023-01-042023-04
 Publication Status: Finally published
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s11071-022-08220-w
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Nonlinear Dynamics
Source Genre: Journal, SCI, Scopus
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 111 Sequence Number: - Start / End Page: 7377 - 7390 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nonlinear-dynamics
Publisher: Springer Nature