14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper WeA2.2

Ekman, Mats (Uppsala Univ.), Hong, Mei (Uppsala Univ.), Söderström, Torsten (Uppsala Univ.)

Separable Nonlinear Least-Squares Approach for Identification of Linear Systems with Errors in Variables

Scheduled for presentation during the Regular Session "Errors in Variables Identification" (WeA2), Wednesday, March 29, 2006, 10:50−11:10, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Errors in Variables Identification


It is well-known that the least-squares identification method generally gives biased parameter estimates when the observed input-output data are corrupted with noise. Previously, an extended version of compensated least-squares (ECLS), based on an overdetermined linear system of equations, was proposed as a method for handling problems where the input and output data are corrupted by white noise. This paper considers the problem where the noise is colored and, thus, extends previous results of the ECLS method. By considering the ECLS problem as a separable nonlinear LS problem, it is shown that the parameters, associated with the noise terms, can be obtained from solving a variable projection minimization problem. The accuracy of the parameter estimates is investigated, and it is also shown that the estimates, under some general assumptions, are consistent.