This paper deals with the identification of errors–in–variables models where the additive input and output noises are mutually correlated white processes. The proposed solution is based on the extension of the dynamic Frisch scheme introduced in (Beghelli et al., 1990). First, a geometric characterization of the whole set of admissible solutions in the noise space is described. Then, a criterion that allows to select the solution of the identification problem inside the locus is proposed. This criterion relies on the properties of a set of high–order Yule–Walker equations. The effectiveness of this identification approach is tested by means of Monte Carlo simulations.

Identification of errors–in–variables models with mutually correlated input and output noises

DIVERSI, ROBERTO;GUIDORZI, ROBERTO;SOVERINI, UMBERTO
2012

Abstract

This paper deals with the identification of errors–in–variables models where the additive input and output noises are mutually correlated white processes. The proposed solution is based on the extension of the dynamic Frisch scheme introduced in (Beghelli et al., 1990). First, a geometric characterization of the whole set of admissible solutions in the noise space is described. Then, a criterion that allows to select the solution of the identification problem inside the locus is proposed. This criterion relies on the properties of a set of high–order Yule–Walker equations. The effectiveness of this identification approach is tested by means of Monte Carlo simulations.
2012
Proceedings of the 16th IFAC Symposium on System Identification
1353
1358
R. Diversi; R. Guidorzi; U. Soverini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/128170
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