Non-parametric identification in dynamic networks

Arne Dankers; Paul Van den Hof

doi:10.1109/CDC.2015.7402759

Non-parametric identification in dynamic networks

Arne Dankers, Paul Van den Hof

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

4 Citations (Scopus)

2 Downloads (Pure)

Abstract

In this paper we present a non-parametric approach to identification in networks. The main advantage of a non-parametric approach is that consistent estimates can be obtained with very little prior knowledge about the system. This is a particularly important consideration for a network identification problem which can easily become very complex with high order dynamics and many inputs. We consider a very general framework for dynamic networks that includes measured variables, external excitation variables, process noise, and sensor noise.

Original language	English
Title of host publication	54th IEEE Conference on Decision and Control (CDC 2015), 15-18 December 2015, Osaka, Japan
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	3487-3492
ISBN (Electronic)	978-1-4799-7885-4
ISBN (Print)	978-1-4799-7884-7
DOIs	https://doi.org/10.1109/CDC.2015.7402759
Publication status	Published - 2015
Event	54th IEEE Conference on Decision and Control (CDC 2015) - "Osaka International Convention Center", Osaka, Japan Duration: 15 Dec 2015 → 18 Dec 2015 Conference number: 54 http://www.cdc2015.ctrl.titech.ac.jp/

Conference

Conference	54th IEEE Conference on Decision and Control (CDC 2015)
Abbreviated title	CDC 2015
Country/Territory	Japan
City	Osaka
Period	15/12/15 → 18/12/15
Internet address	http://www.cdc2015.ctrl.titech.ac.jp/

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10.1109/CDC.2015.7402759

Cite this

@inproceedings{cf1312c33e6849efac9a6a3c3255bf85,

title = "Non-parametric identification in dynamic networks",

abstract = "In this paper we present a non-parametric approach to identification in networks. The main advantage of a non-parametric approach is that consistent estimates can be obtained with very little prior knowledge about the system. This is a particularly important consideration for a network identification problem which can easily become very complex with high order dynamics and many inputs. We consider a very general framework for dynamic networks that includes measured variables, external excitation variables, process noise, and sensor noise.",

author = "Arne Dankers and {Van den Hof}, Paul",

year = "2015",

doi = "10.1109/CDC.2015.7402759",

language = "English",

isbn = "978-1-4799-7884-7",

pages = "3487--3492",

booktitle = "54th IEEE Conference on Decision and Control (CDC 2015), 15-18 December 2015, Osaka, Japan",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "54th IEEE Conference on Decision and Control (CDC 2015), CDC 2015 ; Conference date: 15-12-2015 Through 18-12-2015",

url = "http://www.cdc2015.ctrl.titech.ac.jp/",

}

Dankers, A & Van den Hof, P 2015, Non-parametric identification in dynamic networks. in 54th IEEE Conference on Decision and Control (CDC 2015), 15-18 December 2015, Osaka, Japan. Institute of Electrical and Electronics Engineers, Piscataway, pp. 3487-3492, 54th IEEE Conference on Decision and Control (CDC 2015), Osaka, Japan, 15/12/15. https://doi.org/10.1109/CDC.2015.7402759

Non-parametric identification in dynamic networks. / Dankers, Arne; Van den Hof, Paul.
54th IEEE Conference on Decision and Control (CDC 2015), 15-18 December 2015, Osaka, Japan. Piscataway: Institute of Electrical and Electronics Engineers, 2015. p. 3487-3492.

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

TY - GEN

T1 - Non-parametric identification in dynamic networks

AU - Dankers, Arne

AU - Van den Hof, Paul

N1 - Conference code: 54

PY - 2015

Y1 - 2015

N2 - In this paper we present a non-parametric approach to identification in networks. The main advantage of a non-parametric approach is that consistent estimates can be obtained with very little prior knowledge about the system. This is a particularly important consideration for a network identification problem which can easily become very complex with high order dynamics and many inputs. We consider a very general framework for dynamic networks that includes measured variables, external excitation variables, process noise, and sensor noise.

AB - In this paper we present a non-parametric approach to identification in networks. The main advantage of a non-parametric approach is that consistent estimates can be obtained with very little prior knowledge about the system. This is a particularly important consideration for a network identification problem which can easily become very complex with high order dynamics and many inputs. We consider a very general framework for dynamic networks that includes measured variables, external excitation variables, process noise, and sensor noise.

U2 - 10.1109/CDC.2015.7402759

DO - 10.1109/CDC.2015.7402759

M3 - Conference contribution

SN - 978-1-4799-7884-7

SP - 3487

EP - 3492

BT - 54th IEEE Conference on Decision and Control (CDC 2015), 15-18 December 2015, Osaka, Japan

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 54th IEEE Conference on Decision and Control (CDC 2015)

Y2 - 15 December 2015 through 18 December 2015

ER -

Non-parametric identification in dynamic networks

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