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Some Aspects of Nonlinear Black-Box Modeling in System Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1996 (English)In: Proceedings of the 1996 Conference on Communications, Computing, Control and Signal Processing, Kluwer Academic Publishers, 1996, 431-440 p.Conference paper, Published paper (Refereed)
Abstract [en]

The key problem in system identification is to find a suitable model structure, within which a good model is to be found. Fitting a model within a given structure (parameter estimation) is in most cases a lesser problem. A basic rule in estimation is not to estimate what you already know. In other words, one should utilize prior knowledge and physical insight about the system when selecting the model structure. It is customary to distinguish between three levels of prior knowledge, which have been color-coded as follows.

Place, publisher, year, edition, pages
Kluwer Academic Publishers, 1996. 431-440 p.
Keyword [en]
System identification, Parameter estimation, Nonlinear, Black-box modeling
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-93713DOI: 10.1007/978-1-4615-6281-8_26ISBN: 978-1-4613-7883-9 (print)OAI: oai:DiVA.org:liu-93713DiVA: diva2:629075
Conference
1996 Conference on Communications, Computing, Control and Signal Processing, Stanford, CA, USA, June, 1996
Available from: 2013-06-16 Created: 2013-06-10 Last updated: 2013-08-10

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Ljung, Lennart

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