Box-Jenkins alike identification using nonparametric noise models

Authors:
J. Schoukens;R. Pintelon;Y. Rolain
Affiliations:
Department ELEC, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium;Department ELEC, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium;Department ELEC, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Venue:
Automatica (Journal of IFAC)
Year:
2004

Citing 4
Cited 2

Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets

Automatica (Journal of IFAC)
System identification (2nd ed.): theory for the user

System identification (2nd ed.): theory for the user
Brief Paper: Study of conditional ML estimators in time and frequency-domain system identification

Automatica (Journal of IFAC)
Adaptive smoothing methods for frequency-function estimation

Automatica (Journal of IFAC)

Brief paper: Validity of the standard cross-correlation test for model structure validation

Automatica (Journal of IFAC)
Box-Jenkins identification revisited-Part II: Applications

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

The identification of a SISO linear dynamic system in the presence of output noise disturbances is studied. It is shown that a nonparametric model for the disturbing output noise can be extracted from the raw data, even without estimating a plant model. Next a Box-Jenkins alike identification scheme is proposed using the nonparametric noise model as weighting.