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Identification of switched autoregressive exogenous systems from large noisy datasets
Author(s) -
Hojjatinia Sarah,
Lagoa Constantino M.,
Dabbene Fabrizio
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4968
Subject(s) - autoregressive model , noise (video) , identification (biology) , computer science , process (computing) , star model , integer (computer science) , system identification , distribution (mathematics) , mathematical optimization , algorithm , mathematics , statistics , autoregressive integrated moving average , data mining , artificial intelligence , time series , machine learning , botany , image (mathematics) , biology , programming language , measure (data warehouse) , mathematical analysis , operating system
Summary 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 be applied due to very large computational burden.