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  Finite-time and fixed-time synchronization for a class of memristor-based competitive neural networks with different time scales

Zhao, Y., Ren, S., Kurths, J. (2021): Finite-time and fixed-time synchronization for a class of memristor-based competitive neural networks with different time scales. - Chaos, Solitons and Fractals, 148, 111033.
https://doi.org/10.1016/j.chaos.2021.111033

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 Creators:
Zhao, Yong1, Author
Ren, Shanshan1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: In this paper, finite-time and fixed-time synchronization are considered for a class of memristor-based competitive neural networks(MCNNs) with different time scales. Based on the theory of differential equations with discontinuous right-hand sides, several new sufficient conditions ensuring the finite-time and fixed-time synchronization of MCNNs are obtained by designing proper controllers. Moreover, the settling time is estimated. Finally, a numerical example is given to show the effectiveness and feasibility of our results.

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 Dates: 2021-07
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.chaos.2021.111033
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
 Degree: -

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Title: Chaos, Solitons and Fractals
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 148 Sequence Number: 111033 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/190702
Publisher: Elsevier