English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Finite-time and fixed-time synchronization analysis of shunting inhibitory memristive neural networks with time-varying delays

Kashkynbayev, A., Issakhanov, A., Otkel, M., Kurths, J. (2022): Finite-time and fixed-time synchronization analysis of shunting inhibitory memristive neural networks with time-varying delays. - Chaos, Solitons and Fractals, 156, 111866.
https://doi.org/10.1016/j.chaos.2022.111866

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_27029 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_27029_1
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Kashkynbayev, Ardak1, Author
Issakhanov, Alfarabi1, Author
Otkel, Madina1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: In the present paper, we investigate both the finite-time and fixed-time synchronization of retarded shunting inhibitory cellular neural networks. By constructing suitable Lyapunov functions and feedback control schemes we derive several sufficient conditions to guarantee finite-time and fixed-time synchronization of such networks. Finally, to illustrate the effectiveness of our theoretical results we consider examples with numerical simulations.

Details

show
hide
Language(s): eng - English
 Dates: 2022-02-102022-03
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.chaos.2022.111866
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Health
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: Chaos, Solitons and Fractals
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 156 Sequence Number: 111866 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/190702
Publisher: Elsevier