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.
When Are Errors-in-Variables Aspects Important to Consider in System Identification? / Soderstrom, T; Soverini, U. - ELETTRONICO. - (2022), pp. 315-320. (Intervento presentato al convegno 2022 European Control Conference (ECC) tenutosi a London, United Kingdom nel July 12-15, 2022) [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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
