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• W2113313228 endingPage "1847" @default.
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• W2113313228 abstract "In time-domain subspace methods for identifying linear-time invariant dynamical systems, the model matrices are typically estimated from least squares, based on estimated Kalman filter state sequences and the observed outputs and/or inputs. It is well known that for an infinite amount of data, this least squares estimate of the system matrices is unbiased, when the system order is correctly estimated. However, for a finite amount of data, the obtained model may not be positive real, in which case the algorithm is not able to identify a valid stochastic model. In this note, positive realness is imposed by adding a regularization term to a least squares cost function in the subspace identification algorithm. The regularization term is the trace of a matrix which involves the dynamic system matrix and the output matrix." @default.
• W2113313228 created "2016-06-24" @default.
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• W2113313228 date "2003-10-01" @default.
• W2113313228 modified "2023-10-16" @default.
• W2113313228 title "Identification of positive real models in subspace identification by using regularization" @default.
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• W2113313228 doi "https://doi.org/10.1109/tac.2003.817940" @default.
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