Abstract
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE Conference on Decision and Control, CDC 2018 |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 4713-4718 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5386-1395-5 |
| ISBN (Print) | 978-1-5386-1396-2 |
| DOIs | |
| Publication status | Published - 18 Jan 2019 |
| Event | 57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: 17 Dec 2018 → 19 Dec 2018 Conference number: 57 |
Conference
| Conference | 57th IEEE Conference on Decision and Control, CDC 2018 |
|---|---|
| Abbreviated title | CDC 2018 |
| Country | United States |
| City | Miami |
| Period | 17/12/18 → 19/12/18 |
Cite this
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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. p. 4713-4718 8619436.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Local module identification in dynamic networks using regularized kernel-based methods
AU - Ramaswamy, Karthik R.
AU - Bottegal, Giulio
AU - Van den Hof, Paul M.J.
PY - 2019/1/18
Y1 - 2019/1/18
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85062175611&partnerID=8YFLogxK
U2 - 10.1109/CDC.2018.8619436
DO - 10.1109/CDC.2018.8619436
M3 - Conference contribution
SN - 978-1-5386-1396-2
SP - 4713
EP - 4718
BT - 2018 IEEE Conference on Decision and Control, CDC 2018
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway
ER -