Inverse Regression for the Wiener Class of Systems
2011 (English)Report (Other academic)
The concept of inverse regression has turned out to be quite useful for dimension reduction in regression analysis problems. Using methods like sliced inverse regression (SIR) and directional regression (DR), some high-dimensional nonlinear regression problems can be turned into more tractable low-dimensional problems. Here, the usefulness of inverse regression for identification of nonlinear dynamical systems will be discussed. In particular, it will be shown that the inverse regression methods can be used for identification of systems of the Wiener class, that is, systems consisting of a number of parallel linear subsystems followed by a static multiple-input single-output nonlinearity. For a particular class of input signals, including Gaussian signals, the inverse regression approach makes it possible to estimate the linear subsystems without knowing or estimating the nonlinearity.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 14 p.
LiTH-ISY-R, ISSN 1400-3902 ; 3032
Nonlinear system identification, Nonparametric methods
IdentifiersURN: urn:nbn:se:liu:diva-97974ISRN: LiTH-ISY-R-3032OAI: oai:DiVA.org:liu-97974DiVA: diva2:650872