This paper describes an identification procedure for minimally parametrized multivariable models in the Errors–in–Variables (EIV) context of the Frisch scheme that considers additive white observation noise on the process inputs and outputs. This procedure relies on the geometric approach described in (Guidorzi and Diversi, 2009) that associates EIV models to directions in the noise space. The proposed procedure has been tested by means of a Monte Carlo simulation that confirms its effectiveness.
R. Diversi, R. Guidorzi (2009). Frisch scheme-based identification of multivariable errors-in-variables models. s.l : Elsevier [10.3182/20090706-3-FR-2004.00259].
Frisch scheme-based identification of multivariable errors-in-variables models
DIVERSI, ROBERTO;GUIDORZI, ROBERTO
2009
Abstract
This paper describes an identification procedure for minimally parametrized multivariable models in the Errors–in–Variables (EIV) context of the Frisch scheme that considers additive white observation noise on the process inputs and outputs. This procedure relies on the geometric approach described in (Guidorzi and Diversi, 2009) that associates EIV models to directions in the noise space. The proposed procedure has been tested by means of a Monte Carlo simulation that confirms its effectiveness.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
