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  Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection

Wang, W., Sun, Y., Yuan, M., Wang, Z., Cheng, J., Fan, D., Kurths, J., Luo, X., Wang, C. (2021): Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection. - Chaos, Solitons and Fractals, 150, 111110.
https://doi.org/10.1016/j.chaos.2021.111110

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 Urheber:
Wang, Weiping1, Autor
Sun, Yue1, Autor
Yuan, Manman1, Autor
Wang, Zhen1, Autor
Cheng, Jun1, Autor
Fan, Denggui1, Autor
Kurths, Jürgen2, Autor              
Luo, Xiong1, Autor
Wang, Chunyang1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: This paper presents a new synchronization criterion with a hybrid control approach for multidirectional associative memory neural networks based on memristor (MMAMNNs). That is, the method of impulsive and feedback control combing with the (event) self-triggered mechanism is adopted. However some projective synchronization errors based on state related parameters of MMAMNNs will be affected by the diverse initial conditions. Thus, the new criterion is supported by establishing a novel Lyapunov function combined with the features of such diverse parameters and systems. A collaborative proposed method is designed to make the error of such system converging to zero. Then, the Zeno-behavior is testified to disappear from the proposed programs. Finally, some examples demonstrate the validity of the proposed method and to show its potential application in image protection.

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 Datum: 2021-05-212021-06-182021-09-01
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.chaos.2021.111110
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
 Art des Abschluß: -

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Titel: Chaos, Solitons and Fractals
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 150 Artikelnummer: 111110 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/190702
Publisher: Elsevier