Search: onr:"swepub:oai:DiVA.org:kth-199882" >
Kernel-based system...
Kernel-based system identification from noisy and incomplete intput-output data
-
- Risuleo, Riccardo Sven, 1986- (author)
- KTH,ACCESS Linnaeus Centre,System Identification
-
- Bottegal, Giulio (author)
- KTH,ACCESS Linnaeus Centre
-
- Hjalmarsson, Håkan (author)
- KTH,Reglerteknik,ACCESS Linnaeus Centre
-
(creator_code:org_t)
- IEEE conference proceedings, 2016
- 2016
- English.
-
In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016. - : IEEE conference proceedings. - 9781509018376 ; , s. 2061-2066
- Related links:
-
http://ieeexplore.ie...
-
show more...
-
https://kth.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- In this contribution, we propose a kernel-based method for the identification of linear systems from noisy and incomplete input-output datasets. We model the impulse response of the system as a Gaussian process whose covariance matrix is given by the recently introduced stable spline kernel. We adopt an empirical Bayes approach to estimate the posterior distribution of the impulse response given the data. The noiseless and missing data samples, together with the kernel hyperparameters, are estimated maximizing the joint marginal likelihood of the input and output measurements. To compute the marginal-likelihood maximizer, we build a solution scheme based on the Expectation-Maximization method. Simulations on a benchmark dataset show the effectiveness of the method.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- Electrical Engineering
- Elektro- och systemteknik
- Informations- och kommunikationsteknik
- Information and Communication Technology
Publication and Content Type
- ref (subject category)
- kon (subject category)
Find in a library
To the university's database