Gradient-based iterative parameter estimation for Box–Jenkins systems

This paper presents a gradient-based iterative identification algorithms for Box–Jenkins systems with finite measurement input/output data. Compared with the pseudo-linear regression stochastic gradient approach, the proposed algorithm updates the parameter estimation using all the available data at...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers & mathematics with applications (1987) Jg. 60; H. 5; S. 1200 - 1208
Hauptverfasser: Wang, Dongqing, Yang, Guowei, Ding, Ruifeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2010
Schlagworte:
Algorithms
Box–Jenkins models
Computation
Iterative algorithms
Iterative methods
Mathematical analysis
Mathematical models
Parameter estimation
Recursive dentification
Regression
Signal processing
Stochastic gradient
Stochasticity
Signal processing
Parameter estimation
Recursive dentification
Iterative algorithms
Box–Jenkins models
Stochastic gradient
ISSN:0898-1221, 1873-7668
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper presents a gradient-based iterative identification algorithms for Box–Jenkins systems with finite measurement input/output data. Compared with the pseudo-linear regression stochastic gradient approach, the proposed algorithm updates the parameter estimation using all the available data at each iterative computation (at each iteration), and thus can produce highly accurate parameter estimation. An example is given.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2010.06.001