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- W2686109821 abstract "Abstract An iterative identification method for a linear state-space model with outliers and /or missing data is proposed by applying the Expectation-Maximization (EM) algorithm . The EM algorithm yields a simple identification procedure to facilitate the maximum likelihood (ML) estimation for the state-space models. The missing data case is easily manipulated by the EM algorithm. The outliers arc treated as missing data, and the outliers are detected by maximum a posteriori (MAP) estimate of the occurrence of outlier which is modeled by a Bernoulli sequence, EM algorithm is also applied to the MAP estimation, The fixed-interval smoothed estimate of the state vector is simultaneously obtained, since it is used for the parameter identification, The presen: algorithm is applied to real data to show that the accuracy of the dynamic model is greatly improved by introducing the possibility that the observed data contains outliers." @default.
- W2686109821 created "2017-06-30" @default.
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- W2686109821 date "1990-08-01" @default.
- W2686109821 modified "2023-09-26" @default.
- W2686109821 title "Robust Identification and Smoothing for Linear System with Outliers and Missing Data" @default.
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- W2686109821 doi "https://doi.org/10.1016/s1474-6670(17)51994-1" @default.
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