When recorded signals are corrupted by noise on both input and output sides, standard identification methods give biased parameter estimates, due to the presence of input noise. This paper discusses in what situations such a bias is large and, consequently, when errors-in-variables identification methods should preferably be used.
Soderstrom, T., Soverini, U. (2022). When Are Errors-in-Variables Aspects Important to Consider in System Identification?. 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.23919/ECC55457.2022.9838030].
When Are Errors-in-Variables Aspects Important to Consider in System Identification?
Soverini, U
2022
Abstract
When recorded signals are corrupted by noise on both input and output sides, standard identification methods give biased parameter estimates, due to the presence of input noise. This paper discusses in what situations such a bias is large and, consequently, when errors-in-variables identification methods should preferably be used.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
Soverini_ECC_2022.pdf
accesso aperto
Tipo:
Postprint
Licenza:
Licenza per accesso libero gratuito
Dimensione
494.02 kB
Formato
Adobe PDF
|
494.02 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
