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

Fingerprint

Dynamic Networks
topology
Topology
Forward-backward Algorithm
Bayesian Model Selection
Marginal Likelihood
Hyperparameters
Expectation-maximization Algorithm
Impulse Response
Impulse response
Gaussian Process
Bayesian Approach
Interconnection
transfer functions
Transfer Function
Search Algorithm
Dynamic Systems
Transfer functions
impulses
Dynamical systems

Cite this

Shi, S., Bottegal, G., & van den Hof, P. M. J. (2019). Bayesian topology identification of linear dynamic networks. In 2019 18th European Control Conference, ECC 2019 (pp. 2814-2819). [8795766] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/ECC.2019.8795766
Shi, Shengling ; Bottegal, Giulio ; van den Hof, Paul M.J. / Bayesian topology identification of linear dynamic networks. 2019 18th European Control Conference, ECC 2019. Piscataway : Institute of Electrical and Electronics Engineers, 2019. pp. 2814-2819
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Shi, S, Bottegal, G & van den Hof, PMJ 2019, Bayesian topology identification of linear dynamic networks. in 2019 18th European Control Conference, ECC 2019., 8795766, Institute of Electrical and Electronics Engineers, Piscataway, pp. 2814-2819, 18th European Control Conference, ECC 2019, Naples, Italy, 25/06/19. https://doi.org/10.23919/ECC.2019.8795766

Bayesian topology identification of linear dynamic networks. / Shi, Shengling; Bottegal, Giulio; van den Hof, Paul M.J.

2019 18th European Control Conference, ECC 2019. Piscataway : Institute of Electrical and Electronics Engineers, 2019. p. 2814-2819 8795766.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Shi S, Bottegal G, van den Hof PMJ. Bayesian topology identification of linear dynamic networks. In 2019 18th European Control Conference, ECC 2019. Piscataway: Institute of Electrical and Electronics Engineers. 2019. p. 2814-2819. 8795766 https://doi.org/10.23919/ECC.2019.8795766