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Parameter estimation for Jump Markov Linear Systems
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AI
DOI：10.1016/j.automatica.2021.109949.png)
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Abstract
En 中文
Jump Markov linear systems (JMLS) are a useful model class for capturing abrupt changes in system behaviour that are temporally random, such as when a fault occurs. In many situations, accurate knowledge of the model is not readily available and can be difficult to obtain based on first principles. This paper presents a method for learning parameter values of this model class based on available input-output data using the maximum-likelihood framework. In particular, the expectation- maximisation method is detailed for this model class with attention given to a deterministic and numerically stable implementation. The presented algorithm is compared to state-of-the-art methods on several simulation examples with favourable results. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Jump Markov systems
Nonlinear state estimation
Optimisation
Expectation maximisation
Identification