Order and Structural Dependence Selection of LPV-ARX Models using a Nonnegative Garrote Approach
2010 (English)Report (Other academic)
In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. , 9 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2937
ARX- -Identification--Linear parameter-varying--Order selection
IdentifiersURN: urn:nbn:se:liu:diva-97548ISRN: LiTH-ISY-R-2937OAI: oai:DiVA.org:liu-97548DiVA: diva2:648392