In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as Matlab's PEM and Matlab's N4SID.
A Globally Convergent Matricial Algorithm for Multivariate Spectral Estimation
FERRANTE, AUGUSTO;PAVON, MICHELE
2009
Abstract
In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as Matlab's PEM and Matlab's N4SID.
File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
Newton.pdf
Accesso riservato
Tipologia:
Published (Publisher's Version of Record)
Licenza:
Accesso privato - non pubblico
Dimensione
567.65 kB
Formato
Adobe PDF
|
567.65 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
