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A Least Absolute Shrinkage and Selection Operator (LASSO) for Nonlinear System Identification


Sunil Kukreja, J. Löfberg, Martin J. Brenner

vol. AUT05-11

Identification of parametric nonlinear models involves estimating unknown parameters and detecting its underlying structure. Structure computation is concerned with selecting a subset of parameters to give a parsimonious description of the system which may afford greater insight into the functionality of the system or a simpler controller design. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear systems. The LASSO minimises the residual sum of squares by the addition of a L1 penalty term on the parameter vector of the traditional L2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to linear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. The performance of this LASSO structure detection method was evaluated by using it to estimate the structure of two nonlinear polynomial models. Applicability of the method to more complex systems such as those encountered in aerospace applications was shown by identifying a parsimonious system description of the F/A-18 Active Aeroelastic Wing using flight test data.


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(04)Technical Report

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% Autogenerated BibTeX entry
@TechReport { KukL_f:2005:IFA_2231,
    author={Sunil Kukreja and J. L{\"o}fberg and Martin J. Brenner},
    title={{A Least Absolute Shrinkage and Selection Operator (LASSO)
	  for Nonlinear System Identification}},
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