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- W49344522 abstract "A method is presented for system identification of a discrete linear constant multivariable system, which has unknown system matrices, output structure and noise covariance. The approach is based on an observable canonical structure of the system matrices. The proposed method first identifies the Markov-parameters of the system by correlation analysis, using quaternary input sequences simultaneously, and then estimates the system parameters by a least squares fit. The structural indices (model order) of the system are estimated by an eigenvalue test. From the identified system matrices, the steady-state Kalman matrix and the covariance of the innovation sequences is estimated by an iterative gradient procedure." @default.
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- W49344522 title "Parameter estimation of state-space models for multivariable systems with correlation analysis and method of least squares" @default.
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- W49344522 doi "https://doi.org/10.1016/b978-0-08-022010-9.50055-4" @default.
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