ArticleInternational journal of robust and nonlinear control2020
Identification of switched autoregressive exogenous systems from large noisy datasets.
Article in International journal of robust and nonlinear control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The article introduces novel methodologies for the identification of coefficients of switching autoregressive moving average with exogenous input systems and switched autoregressive exogenous linear models. We consider cases where system's outputs are contaminated by possibly large values of noise for both cases of measurement noise and process noise. It is assumed that only partial information on the probability distribution of the noise is available. Given input-output data, we aim at identifying switched system coefficients and parameters of the distribution of the noise, which are compatible with the collected data. We demonstrate the efficiency of the proposed approach with several academic examples. The method is shown to be effective in the situations where a large number of measurements is available; cases in which previous approaches based on polynomial or mixed-integer optimization cannot he applied due to very large computational burden.
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