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Nonlinear Black Box Models in Systems Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1995 (English)In: Proceedings of the 2nd Russian Swedish Control Conference, 1995, 23-27 p.Conference paper, Published paper (Other academic)
Abstract [en]

The basic idea behind the large family of non-linear black box models is described. It is shown how neural networks, wavelet networks, hinging hyperplanes, kernel methods, fuzzy models, etc all fit to a common framework of basis expansion, using a single 'motherfunction'. Most differences relate to how this single function is expanded to higher regressor dimensions. The estimation theory, algorithmic aspects and applications to dynamical systems are also covered.

Place, publisher, year, edition, pages
1995. 23-27 p.
Keyword [en]
Nonlinear black box, Neural networks, Kernel methods, Dynamical systems
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-93715OAI: oai:DiVA.org:liu-93715DiVA: diva2:629079
Conference
2nd Russian Swedish Control Conference, St. Petersburg, Russia, August, 1995
Available from: 2013-06-16 Created: 2013-06-10 Last updated: 2013-06-16

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Ljung, Lennart

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CiteExportLink to record
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  • apa
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