Abstract
In this paper, we consider the problem of identifying one system (module) embedded in a dynamic network that is disturbed by colored process noise sources, which can possibly be correlated. To achieve this using the direct method for single module identification, we need to formulate a Multi-Input-Multi-Output (MIMO) estimation problem which requires model order selection step for each module in the setup and estimation of large number of parameters. This results in a larger variance in the estimates and an increase in computation complexity. Therefore, we extend the Empirical Bayes Direct Method [1], which handles the above mentioned problems for a Multi-Input-Single-Output (MISO) setup to a MIMO setting by suitably modifying the framework. We keep a parametric model for the desired target module and model the impulse response of all the other modules as independent zero mean Gaussian process governed by a first-order stable spline kernel. The parameters of the target module are obtained by maximizing the marginal likelihood of the output using the Empirical Bayes (EB) approach. To solve this, we use the Expectation Maximization (EM) algorithm which offers computational advantages. Numerical simulation illustrate the advantages of the developed method over existing classical methods.
Original language | English |
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Title of host publication | 59th IEEE Conference on Decision and Control (CDC 2020) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 4348-4353 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-7447-1 |
DOIs | |
Publication status | Published - 11 Jan 2021 |
Event | 59th IEEE Conference on Decision and Control, CDC 2020 - Virtual/Online, Virtual, Jeju Island, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020 Conference number: 59 https://cdc2020.ieeecss.org/ |
Conference
Conference | 59th IEEE Conference on Decision and Control, CDC 2020 |
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Abbreviated title | CDC |
Country/Territory | Korea, Republic of |
City | Virtual, Jeju Island |
Period | 14/12/20 → 18/12/20 |
Internet address |
Funding
This work has received funding from the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 694504).