文章基本信息

标题：Identification of Markov Jump Autoregressive Processes from Large Noisy Data Sets ⁎
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作者：Sarah Hojjatinia ; Constantino Lagoa
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：1077-1083
DOI：10.1016/j.ifacol.2020.12.1300
语种：English
出版社：Elsevier
摘要：AbstractThis paper introduces a novel methodology for the identification of switching dynamics for switched autoregressive linear models. Switching behavior is assumed to follow a Markov model. The system’s outputs are contaminated by possibly large values of measurement noise. Although the procedure provided can handle other noise distributions, for simplicity, it is assumed that the distribution is Normal with unknown variance. Given noisy input-output data, we aim at identifying switched system coefficients, parameters of the noise distribution, dynamics of switching and probability transition matrix of Markovian model. System dynamics are estimated using previous results which exploit algebraic constraints that system trajectories have to satisfy. Switching dynamics are computed with solving a maximum likelihood estimation problem. The efficiency of proposed approach is shown with several academic examples. Although the noise to output ratio can be high, the method is shown to be effective in the situations where a large number of measurements is available.
关键词：KeywordsMarkov Switched ARXIdentification for Control