Identification of nonlinear systems using misspecified predictors
2010 (English)In: 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, 7214-7219 p.Conference paper (Refereed)
Identification of nonlinear systems is an important albeit difficult task. This work considers parameter estimation, using the prediction error method, of the class of models that fit into a nonlinear state space formulation. Finding the optimal predictor for such nonlinear models, if at all possible, often requires significant effort. As an alternative, techniques from indirect inference are used to circumvent this problem. A misspecified predictor, parameterized by a new set of parameters, is used in lieu of the optimal predictor. These new parameters are found numerically by using simulations of the model to be identified. The proposed method is applied to simulation examples and real process data with encouraging results.
Place, publisher, year, edition, pages
2010. 7214-7219 p.
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-48366DOI: 10.1109/CDC.2010.5717249ISI: 000295049108029ScopusID: 2-s2.0-79953144962ISBN: 978-1-4244-7746-3OAI: oai:DiVA.org:kth-48366DiVA: diva2:457427
49th IEEE Conference on Decision and Control (CDC) Location: Atlanta, GA Date: DEC 15-17, 2010
FunderSwedish Research Council, 621-2007-6271
QC 201111222011-11-172011-11-172016-06-01Bibliographically approved