Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics

Köster, F., Yanchuk, S., Lüdge, K. (2022 online): Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics. - IEEE Transactions on Neural Networks and Learning Systems.
https://doi.org/10.1109/TNNLS.2022.3220532

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
Köster_2022_Master_Memory_Function_for_Delay-Based_Reservoir_Computers_With_Single-Variable_Dynamics.pdf (Verlagsversion), 2MB
Name:
Köster_2022_Master_Memory_Function_for_Delay-Based_Reservoir_Computers_With_Single-Variable_Dynamics.pdf
Beschreibung:
-
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
ttps://creativecommons.org/licenses/by/4.0/

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Köster, Felix1, Autor
Yanchuk, Serhiy2, Autor              
Lüdge, Kathy1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey–Glass or Stuart–Landau-like systems, but also to reservoirs whose dynamical model is not available.

Details

einblenden:
ausblenden:
Sprache(n): eng - Englisch
 Datum: 2022-11-182022-11-18
 Publikationsstatus: Online veröffentlicht
 Seiten: 14
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/TNNLS.2022.3220532
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
Model / method: Machine Learning
OATYPE: Hybrid Open Access
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: IEEE Transactions on Neural Networks and Learning Systems
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-on-neural-networks-and-learning-systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)