Optimal experiment design for identification of grey-box models

Payman Sadegh; Henrik Melgaard; Henrik Madsen; Jan Holst

doi:10.1109/ACC.1994.751709

Optimal experiment design for identification of grey-box models

Payman Sadegh, Henrik Melgaard, Henrik Madsen, Jan Holst

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Abstract

Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure of average information, and the relationship between the choice of information related criteria and some estimators (MAP and MLE) is established. A continuous time physical model of the heat dynamics of a building is considered and the results show that performing an optimal experiment corresponding to a MAP estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest.

Original language	English
Title of host publication	Proceedings of the American Control Conference
Volume	Volume 1
Publisher	IEEE
Publication date	1994
Pages	132-137
ISBN (Print)	07-80-31783-1
DOIs	https://doi.org/10.1109/ACC.1994.751709
Publication status	Published - 1994
Event	1994 American Control Conference - Baltimore, MD, United States Duration: 29 Jun 1994 → 1 Jul 1994

Conference

Conference	1994 American Control Conference
Country/Territory	United States
City	Baltimore, MD
Period	29/06/1994 → 01/07/1994

Bibliographical note

Copyright: 1994 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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title = "Optimal experiment design for identification of grey-box models",

abstract = "Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure of average information, and the relationship between the choice of information related criteria and some estimators (MAP and MLE) is established. A continuous time physical model of the heat dynamics of a building is considered and the results show that performing an optimal experiment corresponding to a MAP estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest.",

author = "Payman Sadegh and Henrik Melgaard and Henrik Madsen and Jan Holst",

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AU - Holst, Jan

N1 - Copyright: 1994 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

PY - 1994

Y1 - 1994

N2 - Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure of average information, and the relationship between the choice of information related criteria and some estimators (MAP and MLE) is established. A continuous time physical model of the heat dynamics of a building is considered and the results show that performing an optimal experiment corresponding to a MAP estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest.

AB - Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure of average information, and the relationship between the choice of information related criteria and some estimators (MAP and MLE) is established. A continuous time physical model of the heat dynamics of a building is considered and the results show that performing an optimal experiment corresponding to a MAP estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest.

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DO - 10.1109/ACC.1994.751709

M3 - Article in proceedings

SN - 07-80-31783-1

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SP - 132

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BT - Proceedings of the American Control Conference

PB - IEEE

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Y2 - 29 June 1994 through 1 July 1994

ER -

Optimal experiment design for identification of grey-box models

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