Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching Networks

Hu, B., Yu, X., Guan, Z.-H., Kurths, J., Chen, G. (2021): Hybrid Neural Adaptive Control for Practical Tracking of Markovian Switching Networks. - IEEE Transactions on Neural Networks and Learning Systems, 32, 5, 2157-2168.
https://doi.org/10.1109/TNNLS.2020.3001009

Item is

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Hu, Bin1, Autor
Yu, Xinghuo1, Autor
Guan, Zhi-Hong1, Autor
Kurths, Jürgen2, Autor              
Chen, Guanrong1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent adaptation law built on neural networks (NNs) is presented for efficient distributed adaptive control. It is shown that the proposed control scheme can guarantee a stable closed-loop error system for any positive control gain and tuning gain. The tracking error is demonstrated to be practically uniformly exponentially stable with a threshold in the mean-square sense. This study further reveals how the topological structure affects the NN function, by measuring the influence of the switched topologies on the learning performance.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2021-06-22
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/TNNLS.2020.3001009
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
 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: 32 (5) Artikelnummer: - Start- / Endseite: 2157 - 2168 Identifikator: Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Anderer: 2162-237X
ISSN: 2162-237X
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-on-neural-networks-and-learning-systems