Errors-in-Variables (EIV) models consider the presence of additive errors on the measures of all measurable attributes of a process. Traditional identification procedures for these processes rely on the properties of the covariance matrix of the observations and on its decomposition into the sum of a matrix associated with the (unknown) noiseless sequences and of the observation noise covariance matrix. This paper makes reference, in a behavioural framework, to the observed noisy trajectories and performs a decomposition of these trajectories into a regular part, defininig the associated behaviour of the process, and a noise part. The Monte Carlo simulations that have been performed show that the proposed approach leads to accurate estimates of both the system parameters and the noise variances.

A behavioural approach in EIV identification: the SISO case

GUIDORZI, ROBERTO;DIVERSI, ROBERTO
2012

Abstract

Errors-in-Variables (EIV) models consider the presence of additive errors on the measures of all measurable attributes of a process. Traditional identification procedures for these processes rely on the properties of the covariance matrix of the observations and on its decomposition into the sum of a matrix associated with the (unknown) noiseless sequences and of the observation noise covariance matrix. This paper makes reference, in a behavioural framework, to the observed noisy trajectories and performs a decomposition of these trajectories into a regular part, defininig the associated behaviour of the process, and a noise part. The Monte Carlo simulations that have been performed show that the proposed approach leads to accurate estimates of both the system parameters and the noise variances.
Proceedings of the 7th Vienna International Conference on Mathematical Modelling (MATHMOD 2012)
143
147
R. Guidorzi; R. Diversi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/153108
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