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The paper introduces novel methodologies for the identification ofcoefficients of switched autoregressive and switched autoregressive exogenouslinear models. We consider cases which system's outputs are contaminated bypossibly large values of noise for the both case of measurement noise inswitched autoregressive models and process noise in switched autoregressiveexogenous models. It is assumed that only partial information on theprobability distribution of the noise is available. Given input-output data, weaim at identifying switched system coefficients and parameters of thedistribution of the noise, which are compatible with the collected data. Wedemonstrate the efficiency of the proposed approach with several academicexamples. The method is shown to be extremely effective in the situations wherea large number of measurements is available; cases in which previous approachesbased on polynomial or mixed-integer optimization cannot be applied due to verylarge computational burden.

Identification of Switched Autoregressive and Switched Autoregressive Exogenous Systems from Large Noisy Data Sets