A covariance extension approach to identification of time series
2000 (English)In: Automatica, ISSN 0005-1098, Vol. 36, no 3, 379-398 p.Article in journal (Refereed) Published
In this paper we consider a three-step procedure far identification of time series, based on covariance extension and model reduction, and we present a complete analysis of its statistical convergence properties. A partial covariance sequence is estimated from statistical data. Then a high-order maximum-entropy model is determined, which is finally approximated by a lower-order model by stochastically balanced model reduction. Such procedures have been studied before, in various combinations, but an overall convergence analysis comprising all three steps has been lacking. Supposing the data is generated from a true finite-dimensional system which is minimum phase, it is shown that the transfer function of the estimated system tends in H-infinity to the true transfer function as the data length tends to infinity, if the covariance extension and the model reduction is done properly. The proposed identification procedure, and some variations of it, are evaluated by simulations.
Place, publisher, year, edition, pages
2000. Vol. 36, no 3, 379-398 p.
error-bounds, realization, spectra
IdentifiersURN: urn:nbn:se:kth:diva-19523ISI: 000085209200004OAI: oai:DiVA.org:kth-19523DiVA: diva2:338215
QC 201005252010-08-102010-08-10Bibliographically approved