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 languageEnglish
    Title of host publicationProceedings of the American Control Conference
    VolumeVolume 1
    PublisherIEEE
    Publication date1994
    Pages132-137
    ISBN (Print)07-80-31783-1
    DOIs
    Publication statusPublished - 1994
    Event1994 American Control Conference - Baltimore, MD, United States
    Duration: 29 Jun 19941 Jul 1994

    Conference

    Conference1994 American Control Conference
    CountryUnited States
    CityBaltimore, MD
    Period29/06/199401/07/1994

    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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