Local module identification in dynamic networks using regularized kernel-based methods

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

In order to identify a specific system (module) of interest embedded in a dynamic network, one typically has to formulate a multi-input single-output (MISO) identification problem which requires to identify all modules in the MISO structure, and determine their model order. While the former task poses the problem of estimating a large number of parameters that are of no interest to the experimenter, the latter task may result computationally challenging in large-size networks. To avoid these issues and increase the accuracy of the identified module of interest, we use regularized kernel-based methods. Keeping a parametric model for the module of interest, we model the impulse response of the remaining modules in the MISO structure as zero mean Gaussian vectors with covariance matrix (kernel) given by the first-order stable spline kernel, accounting also for the noise model affecting the output of the target model. Using an Empirical Bayes (EB) approach, the target-module parameters are estimated by maximizing the marginal likelihood of the module output. The related optimization problem is solved using the Expectation-Maximization (EM) algorithm. Numerical experiments illustrate the potentials of the introduced method in comparison with the state-of-the-art techniques for local identification.
TaalEngels
Titel2018 IEEE Conference on Decision and Control, CDC 2018
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's4713-4718
Aantal pagina's6
ISBN van elektronische versie978-1-5386-1395-5
ISBN van geprinte versie978-1-5386-1396-2
DOI's
StatusGepubliceerd - 18 jan 2019
Evenement57th IEEE Conference on Decision and Control, CDC 2018 - Miami, Verenigde Staten van Amerika
Duur: 17 dec 201819 dec 2018
Congresnummer: 57

Congres

Congres57th IEEE Conference on Decision and Control, CDC 2018
Verkorte titelCDC 2018
LandVerenigde Staten van Amerika
StadMiami
Periode17/12/1819/12/18

Citeer dit

Ramaswamy, K. R., Bottegal, G., & Van den Hof, P. M. J. (2019). Local module identification in dynamic networks using regularized kernel-based methods. In 2018 IEEE Conference on Decision and Control, CDC 2018 (blz. 4713-4718). [8619436] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/CDC.2018.8619436
Ramaswamy, Karthik R. ; Bottegal, Giulio ; Van den Hof, Paul M.J./ Local module identification in dynamic networks using regularized kernel-based methods. 2018 IEEE Conference on Decision and Control, CDC 2018. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 4713-4718
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Ramaswamy, KR, Bottegal, G & Van den Hof, PMJ 2019, Local module identification in dynamic networks using regularized kernel-based methods. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8619436, Institute of Electrical and Electronics Engineers, Piscataway, blz. 4713-4718, Miami, Verenigde Staten van Amerika, 17/12/18. DOI: 10.1109/CDC.2018.8619436

Local module identification in dynamic networks using regularized kernel-based methods. / Ramaswamy, Karthik R.; Bottegal, Giulio; Van den Hof, Paul M.J.

2018 IEEE Conference on Decision and Control, CDC 2018. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 4713-4718 8619436.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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AB - In order to identify a specific system (module) of interest embedded in a dynamic network, one typically has to formulate a multi-input single-output (MISO) identification problem which requires to identify all modules in the MISO structure, and determine their model order. While the former task poses the problem of estimating a large number of parameters that are of no interest to the experimenter, the latter task may result computationally challenging in large-size networks. To avoid these issues and increase the accuracy of the identified module of interest, we use regularized kernel-based methods. Keeping a parametric model for the module of interest, we model the impulse response of the remaining modules in the MISO structure as zero mean Gaussian vectors with covariance matrix (kernel) given by the first-order stable spline kernel, accounting also for the noise model affecting the output of the target model. Using an Empirical Bayes (EB) approach, the target-module parameters are estimated by maximizing the marginal likelihood of the module output. The related optimization problem is solved using the Expectation-Maximization (EM) algorithm. Numerical experiments illustrate the potentials of the introduced method in comparison with the state-of-the-art techniques for local identification.

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Ramaswamy KR, Bottegal G, Van den Hof PMJ. Local module identification in dynamic networks using regularized kernel-based methods. In 2018 IEEE Conference on Decision and Control, CDC 2018. Piscataway: Institute of Electrical and Electronics Engineers. 2019. blz. 4713-4718. 8619436. Beschikbaar vanaf, DOI: 10.1109/CDC.2018.8619436