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System Identification Based On Errors-In-Variables System Models, Hyundeok Kang
System Identification Based On Errors-In-Variables System Models, Hyundeok Kang
LSU Doctoral Dissertations
We study the identification problem for errors-in-variables (EIV) systems. Such an EIV model assumes that the measurement data at both input and output of the system involve corrupting noises. The least square (LS) algorithm has been widely used in this area. However, it results in biased estimates for the EIV-based system identification. In contrast, the total least squares (TLS) algorithm is unbiased, which is now well-known, and has been effective for estimating the system parameters in the EIV system identification.
In this dissertation, we first show that the TLS algorithm computes the approximate maximum likelihood estimate (MLE) of the system …