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• W2069371232 abstract "Parameter estimation when the measurement information may be incomplete is discussed. An ARX model is used as a basic system representation. The presentation covers both missing output and missing input. First reconstruction of the missing values is discussed. The reconstruction is based on a state-space formulation of the system, and is performed using Kalman filtering or fixed-interval smoothing formulas. Several approaches to the identification problem are presented, including a new method based on the EM (expectation maximization) algorithm. The different approaches are tested and compared using Monte Carlo simulations. The choice of method is always a tradeoff between estimation accuracy and computational complexity. According to the simulations the gain in accuracy using the EM method can be considerable if many data are missing.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
• W2069371232 created "2016-06-24" @default.
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• W2069371232 date "1993-05-01" @default.
• W2069371232 modified "2023-10-01" @default.
• W2069371232 title "Identification of ARX-models subject to missing data" @default.
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• W2069371232 doi "https://doi.org/10.1109/9.277253" @default.
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