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Black-Box Models from Input-Output Measurements
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2001 (English)In: Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference, IEEE , 2001, Vol. 1, 138-146 p.Conference paper, Published paper (Refereed)
Abstract [en]

A black-box model of a system is one that does not use any particular prior knowledge of the character or physics of the relationships involved. It is therefore more a question of "curve- fitting" than "modeling". In this presentation several examples of such black-box model structures will be given. Both linear and non-linear structures are treated. Relationships between linear models, fuzzy models, neural networks and classical non-parametric models are discussed. Some reasons for the usefulness of these model types will also be given. Ways to fit black box structures to measured input-output data are described, as well as the more fundamental (statistical) properties of the resulting models.

Place, publisher, year, edition, pages
IEEE , 2001. Vol. 1, 138-146 p.
Keyword [en]
Black-box, Signal processing, Parameter estimation
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-90830DOI: 10.1109/IMTC.2001.928802ISBN: 0-7803-6646-8 (print)OAI: oai:DiVA.org:liu-90830DiVA: diva2:616260
Conference
18th IEEE Instrumentation and Measurement Technology Conference, 21-23 May 2001, Budapest, Hungary
Available from: 2013-04-15 Created: 2013-04-07 Last updated: 2015-05-25

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Ljung, Lennart

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  • sv-SE
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Output format
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