l2-optimal approximate modelling problem

Berend Roorda*, Christiaan Heij

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingProfessionalpeer-review

1 Citation (Scopus)


In this paper, which to some extend summarizes [3], we present a novel approach for the modelling of multivariable time series. The model class consists of linear systems, i.e., the solution sets of linear difference equations. Restricting the model order, the aim is to determine a model with minimal l2-distance from the observed time series. We propose an iterative algorithm for the nonlinear problem of identifying optimal models, using isometric state representations. Attractive aspects of the proposed method are that the model error is measured globally, that it can be applied for multi-input, multi-output systems and that no prior distinction between inputs and outputs is required. We also describe the link between isometric state representations and normalized coprime factorizations, and make some remarks on model uncertainty.

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Number of pages4
Publication statusPublished - 1993
Externally publishedYes
EventProceedings of the 32nd IEEE Conference on Decision and Control. Part 2 (of 4) - San Antonio, TX, USA
Duration: 15 Dec 199317 Dec 1993

Publication series

SeriesProceedings of the IEEE Conference on Decision and Control


ConferenceProceedings of the 32nd IEEE Conference on Decision and Control. Part 2 (of 4)
CitySan Antonio, TX, USA


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