Predictor input selection for direct identification in dynamic networks

A.G. Dankers; P.M.J. Hof, Van den; P.S.C. Heuberger

Predictor input selection for direct identification in dynamic networks

A.G. Dankers, P.M.J. Hof, Van den, P.S.C. Heuberger

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	Proceedings of the 52nd IEEE Conference on Decision and Control, 10-13 December 2013, Firenze
Place of Publication	Firenze, Italy
Publisher	Institute of Electrical and Electronics Engineers
Publication status	Published - 2013
Event	52nd IEEE Conference on Decision and Control (CDC 2013) - Florence, Italy Duration: 10 Dec 2013 → 13 Dec 2013 Conference number: 52

Conference

Conference	52nd IEEE Conference on Decision and Control (CDC 2013)
Abbreviated title	CDC 2013
Country/Territory	Italy
City	Florence
Period	10/12/13 → 13/12/13

Cite this

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title = "Predictor input selection for direct identification in dynamic networks",

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.",

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year = "2013",

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booktitle = "Proceedings of the 52nd IEEE Conference on Decision and Control, 10-13 December 2013, Firenze",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "52nd IEEE Conference on Decision and Control (CDC 2013), CDC 2013 ; Conference date: 10-12-2013 Through 13-12-2013",

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Dankers, AG, Hof, Van den, PMJ & Heuberger, PSC 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, Firenze, Italy, 52nd IEEE Conference on Decision and Control (CDC 2013), Florence, Italy, 10/12/13.

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Predictor input selection for direct identification in dynamic networks

AU - Dankers, A.G.

AU - Hof, Van den, P.M.J.

AU - Heuberger, P.S.C.

N1 - Conference code: 52

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Proceedings of the 52nd IEEE Conference on Decision and Control, 10-13 December 2013, Firenze

PB - Institute of Electrical and Electronics Engineers

CY - Firenze, Italy

T2 - 52nd IEEE Conference on Decision and Control (CDC 2013)

Y2 - 10 December 2013 through 13 December 2013

ER -

Predictor input selection for direct identification in dynamic networks

Abstract

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