Predictor input selection for direct identification in dynamic networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

13 Citations (Scopus)
1 Downloads (Pure)

Abstract

In the literature methods have been proposed which enable consistent estimates of modules embedded in complex dynamic networks. In this paper the network extension of the so called closed-loop Direct Method is investigated. Currently, for this method the variables which must be included in the predictor model are not considered as a user choice. In this paper it is shown that there is some freedom as to which variables to include in the predictor model as inputs, and still obtain consistent estimates of the module of interest. Conditions on this choice of predictor inputs are presented.
Original languageEnglish
Title of host publicationProceedings of the 52nd IEEE Conference on Decision and Control, 10-13 December 2013, Firenze
Place of PublicationFirenze, Italy
PublisherInstitute of Electrical and Electronics Engineers
Publication statusPublished - 2013
Event52nd IEEE Conference on Decision and Control (CDC 2013) - Florence, Italy
Duration: 10 Dec 201313 Dec 2013
Conference number: 52

Conference

Conference52nd IEEE Conference on Decision and Control (CDC 2013)
Abbreviated titleCDC 2013
CountryItaly
CityFlorence
Period10/12/1313/12/13

Cite this

Dankers, A. G., Hof, Van den, P. M. J., & Heuberger, P. S. C. (2013). Predictor input selection for direct identification in dynamic networks. In Proceedings of the 52nd IEEE Conference on Decision and Control, 10-13 December 2013, Firenze Institute of Electrical and Electronics Engineers.