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

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 Zusammenfassung: 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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 Datum: 2021-07
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: 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
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Titel: Chaos, Solitons and Fractals
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 148 Artikelnummer: 111033 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/190702
Publisher: Elsevier