Bayesian topology identification of linear dynamic networks

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Abstract

In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying the connectivity of networks of transfer functions, without the need to estimate the dynamics. The algorithm employs a Bayesian measure and a forward-backward search algorithm. To obtain the Bayesian measure, the impulse responses of network modules are modeled as Gaussian processes and the hyperparameters are estimated by marginal likelihood maximization using the expectation-maximization algorithm. Numerical results demonstrate the effectiveness of this method.

Original languageEnglish
Title of host publication2019 18th European Control Conference, ECC 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages2814-2819
Number of pages6
ISBN (Electronic)978-3-907144-00-8
DOIs
Publication statusPublished - 1 Jun 2019
Event18th European Control Conference, ECC 2019 - Naples, Italy
Duration: 25 Jun 201928 Jun 2019

Conference

Conference18th European Control Conference, ECC 2019
CountryItaly
CityNaples
Period25/06/1928/06/19

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