Using Manifold Learning for Nonlinear System Identification
2007 (English)In: Proceedings of the 7th IFAC Symposium on Nonlinear Control Systems, 2007, 170-175 p.Conference paper (Refereed)
A high-dimensional regression space usually causes problems in nonlinear system identiﬁcation.However, if the regression data are contained in (or spread tightly around) some manifold, thedimensionality can be reduced. This paper presents a use of dimension reduction techniques tocompose a two-step identiﬁcation scheme suitable for high-dimensional identiﬁcation problems withmanifold-valued regression data. Illustrating examples are also given.
Place, publisher, year, edition, pages
2007. 170-175 p.
Nonlinear system identification, Dimension reduction techniques, Manifold learning
Engineering and Technology Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-42219DOI: 10.3182/20070822-3-ZA-2920.00029Local ID: 61637ISBN: 978-3-902661-28-9OAI: oai:DiVA.org:liu-42219DiVA: diva2:263074
7th IFAC Symposium on Nonlinear Control Systems, Pretoria, South Africa, August, 2007