Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  State estimation of fractional-order delayed memristive neural networks

Bao, H., Cao, J., Kurths, J. (2018): State estimation of fractional-order delayed memristive neural networks. - Nonlinear Dynamics, 94, 2, 1215-1225.
https://doi.org/10.1007/s11071-018-4419-3

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
8301.pdf (Verlagsversion), 823KB
 
Datei-Permalink:
-
Name:
8301.pdf
Beschreibung:
-
Sichtbarkeit:
Privat
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Bao, H.1, Autor
Cao, J.1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: This paper focuses on designing state estimators for fractional-order memristive neural networks (FMNNs) with time delays. It is meaningful to propose a suitable state estimator for FMNNs because of the following two reasons: (1) different initial conditions of memristive neural networks (MNNs) may cause parameter mismatch; (2) state estimation approaches and theories for integer-order neural networks cannot be directly extended and used to deal with fractional-order neural networks. The present paper first investigates state estimation problem for FMNNs. By means of Lyapunov functionals and fractional-order Lyapunov methods, sufficient conditions are built to ensure that the estimation error system is asymptotically stable, which are readily solved by MATLAB LMI Toolbox. Ultimately, two examples are presented to show the effectiveness of the proposed theorems.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2018
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1007/s11071-018-4419-3
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8301
Research topic keyword: Complex Networks
Research topic keyword: Extremes
Research topic keyword: Nonlinear Dynamics
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Nonlinear Dynamics
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 94 (2) Artikelnummer: - Start- / Endseite: 1215 - 1225 Identifikator: Anderer: 1573-269X
ISSN: 0924-090X
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nonlinear-dynamics
Publisher: Springer