Title :
Identification of multivariable errors-in-variables models
Author :
Castaldi, P. ; Diversi, R. ; Guidorzi, R. ; Soverini, U.
Author_Institution :
Dipt. di Elettron., Inf. e Sist., Univ. di Bologna, Bologna, Italy
fDate :
Aug. 31 1999-Sept. 3 1999
Abstract :
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.
Keywords :
MIMO systems; autoregressive moving average processes; parameter estimation; ARMAX decomposition; MIMO EIV processes; congruence conditions; multivariable errors-in-variables model identification; noise covariance matrices; noisy sequences; prediction error method; Computational modeling; Covariance matrices; MIMO; Noise; Noise measurement; Numerical models; Predictive models; System Identification; errors-in-variables models; multivariable models; optimal ARMAX predictor;
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5
