Incorporating Term selection into nonlinear block structured system identification

Mohammad Rasouli, David T. Westwick, W. D. Rosehart

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Subset selection and shrinkage methods locate and remove insignificant terms from identified models. The least absolute shrinkage and selection operator (Lasso) is a term selection method that shrinks some coefficients and sets others to zero. In this paper, the incorporation of constraints (such as Lasso) into the linear and/or nonlinear parts of a Separable Nonlinear Least Squares algorithm is addressed and its application to the identification of block-structured models is considered. As an example, this method is applied to a Hammerstein model consisting of a nonlinear static block, represented by a Tchebyshev polynomial, in series with a linear dynamic system, modeled by a bank of Laguerre filters. Simulations showed that the Lasso based method was able to identify the model structure correctly, or with mild over-modeling, even in the presence of significant output noise.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 American Control Conference, ACC 2010
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)9781424474264
StatePublished - 2010

Publication series

NameProceedings of the 2010 American Control Conference, ACC 2010

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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