Abstract
Outliers are one of the common factors that affect the quality of routine operational data. In this work, we propose a robust identification approach for the Switched Markov Autoregressive eXogeneous (SMARX) system to deal with outliers. The robust identification problem is formulated by imposing Student’s t-distribution to the noise model. The Expectation-Maximization algorithm is adopted to estimate the parameters of both the continuous dynamics described by local ARX models and discrete dynamics described by Hidden Markov Model. The advantages of the proposed approach are demonstrated through a numerical simulation.
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