Parameter estimation for Jump Markov Linear Systems

Title: Parameter estimation for Jump Markov Linear Systems
Creator: Balenzuela, Mark P.; Wills, Adrian G.; Renton, Christopher; Ninness, Brett
Relation: Automatica Vol. 135, no. 109949
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2021.109949
Publisher: Elsevier
Resource Type: journal article
Date: 2022
Description: 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.
Subject: Jump Markov systems; nonlinear state estimation; optimisation; expectation maximisation; identification
Identifier: http://hdl.handle.net/1959.13/1463536
Identifier: uon:46763
Identifier: ISSN:0005-1098
Language: eng
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