Identification of multivariable errors-in-variables models

Identification of multivariable errors-in-variables models

The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables models (EIV). Starting from the ARMAX decomposition of MIMO EIV processes and congruence conditions between noisy sequences and the constraints of EIV representations, the si...

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Bibliographic Details
Published in:1999 European Control Conference (ECC) pp. 969 - 974
Main Authors: Castaldi, P., Diversi, R., Guidorzi, R., Soverini, U.
Format: Conference Proceeding
Language:English
Published: IEEE 01-08-1999
Subjects:
Computational modeling
Covariance matrices
errors-in-variables models
MIMO
multivariable models
Noise
Noise measurement
Numerical models
optimal ARMAX predictor
Predictive models
System Identification
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Summary:The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables models (EIV). Starting from the ARMAX decomposition of MIMO EIV processes and congruence conditions between noisy sequences and the constraints of EIV representations, the simultaneous estimate of the model parameters and of the noise covariance matrices is obtained. Numerical simulations are included to illustrate the effectiveness of the proposed algorithm.
ISBN:3952417351
9783952417355
DOI:10.23919/ECC.1999.7099433