Identi cation of Positive Real Models
نویسندگان
چکیده
{ In subspace methods for system identiication, the system matrices can be estimated from least squares, based on estimated Kalman lter state sequences and the observed inputs and outputs. It is well known that for an innnite amount of data, this least squares estimate of the system matrices is unbiased, when the system order is correctly estimated. However, for a nite amount of data, the estimated system may not be positive real. In this paper, positive realness is obtained by adding a regularization term in the least squares cost function. The regularization term is the trace of a T. Van Gestel is a Research Assistant with the Fund for Scientiic Research-Flanders (FWO-Vlaanderen). J. Suykens is a Postdoctoral Researcher with the FWO-Vlaanderen. 1 matrix which involves the dynamic system matrix and the output matrix.
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تاریخ انتشار 2000