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Partially-Linear Least-Squares Regularized Regression for System Identification | IEEE Journals & Magazine | IEEE Xplore

Partially-Linear Least-Squares Regularized Regression for System Identification


Abstract:

In this technical note, we propose a partially-linear least-squares regularized regression (PL-LSRR) method for system identification. This method identifies a general no...Show More

Abstract:

In this technical note, we propose a partially-linear least-squares regularized regression (PL-LSRR) method for system identification. This method identifies a general nonlinear function as a sum of two functions which come from a linear and a nonlinear function space respectively. Both the linear and nonlinear functions can involve all regressors. Therefore, the PL-LSRR can make use of the partially-linear structure of a given system to reduce prediction errors more efficiently than exiting partially-linear identification methods. Two examples show that the PL-LSRR can reduce prediction errors and estimate the true linear expansion of the system well.
Published in: IEEE Transactions on Automatic Control ( Volume: 54, Issue: 11, November 2009)
Page(s): 2637 - 2641
Date of Publication: 20 October 2009

ISSN Information:


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