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Neural Processing Letters
Erschienen in: Neural Processing Letters 5/2021

24.06.2021

Continuous-Time Varying Complex QR Decomposition via Zeroing Neural Dynamics

verfasst von: Vasilios N. Katsikis, Spyridon D. Mourtas, Predrag S. Stanimirović, Yunong Zhang

Erschienen in: Neural Processing Letters | Ausgabe 5/2021

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Abstract

QR decomposition (QRD) is of fundamental importance for matrix factorization in both real and complex cases. In this paper, by using zeroing neural dynamics method, a continuous-time model is proposed for solving the time-varying problem of QRD in real-time. The proposed dynamics use time derivative information from a known real or complex matrix. Furthermore, its theoretical analysis is provided to substantiate the convergence and effectiveness of solving the time-varying QRD problem. In addition, numerical experiments in four different-dimensional time-varying matrices show that the proposed model is effective for solving the time-varying QRD problem both in the case of a real or a complex matrix as input.
Vorheriger Artikel Semi-Supervised Clustering for Financial Risk Analysis
Nächster Artikel A faster and better robustness zeroing neural network for solving dynamic Sylvester equation

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Metadaten
Titel
Continuous-Time Varying Complex QR Decomposition via Zeroing Neural Dynamics
verfasst von
Vasilios N. Katsikis
Spyridon D. Mourtas
Predrag S. Stanimirović
Yunong Zhang
Publikationsdatum
24.06.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 5/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10566-y

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