Combining the best linear approximation and dimension reduction to identify the linear blocks of parallel Wiener systems
2013 (English)In: Proceedings of the 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2013, 372-377 p.Conference paper (Refereed)
A Wiener model is a fairly simple, well known, and often used nonlinear block- oriented black-box model. A possible generalization of the class of Wiener models lies in the parallel Wiener model class. This paper presents a method to estimate the linear time-invariant blocks of such parallel Wiener models from input/output data only. The proposed estimation method combines the knowledge obtained by estimating the best linear approximation of a nonlinear system with the MAVE dimension reduction method to estimate the linear time- invariant blocks present in the model. The estimation of the static nonlinearity boils down to a standard static nonlinearity estimation problem starting from input-output data once the linear blocks are known.
Place, publisher, year, edition, pages
2013. 372-377 p.
, IFAC-PapersOnLine, ISSN 2405-8963 ; 46(11)
Nonlinear system identification, Nonlinear systems
IdentifiersURN: urn:nbn:se:liu:diva-104001DOI: 10.3182/20130703-3-FR-4038.00026OAI: oai:DiVA.org:liu-104001DiVA: diva2:694125
11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, Caen, France, July 3-5, 2013