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Nonlinear Black Box Models in System Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1997 (English)In: Proceedings of the 1997 IFAC International Symposum on Advanced Control of Chemical Processes, 1997, 1-13 p.Conference paper, Published paper (Refereed)
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
1997. 1-13 p.
Keyword [en]
Nonlinear black box, Neural networks, Kernel methods, Dynamical systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-91579OAI: oai:DiVA.org:liu-91579DiVA: diva2:626299
Conference
1997 IFAC International Symposum on Advanced Control of Chemical Processes, Banff, Alberta, Canada, June, 1997
Available from: 2013-06-07 Created: 2013-04-28 Last updated: 2013-06-07

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Ljung, Lennart

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