Nonlinear Black Box Models in Systems Identification
1995 (English)In: Proceedings of the 2nd Russian Swedish Control Conference, 1995, 23-27 p.Conference paper (Other academic)
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.
Nonlinear black box, Neural networks, Kernel methods, Dynamical systems
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-93715OAI: oai:DiVA.org:liu-93715DiVA: diva2:629079
2nd Russian Swedish Control Conference, St. Petersburg, Russia, August, 1995