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Incorporating term selection into nonlinear block structured system identification

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3 Author(s)
Mohammad Rasouli ; Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary 2500 University Dr. N.W., Alberta, T2N 1N4, Canada ; David T. Westwick ; W. D. Rosehart

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.

Published in:

Proceedings of the 2010 American Control Conference

Date of Conference:

June 30 2010-July 2 2010