Kernel-based system identification from noisy and incomplete input-output data

Riccardo S. Risuleo, Giulio Bottegal, Håkan Hjalmarsson

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

3 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2016 IEEE 55th Conference on Decision and Control (CDC) : ARIA Resort & Casino, December 12-14, 2016, Las Vegas, USA
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-5090-1837-6
ISBN (Print)978-1-5090-1838-3
Publication statusPublished - 2016
Externally publishedYes
Event55th IEEE Conference on Decision and Control (CDC 2016) - Aria Resort and Casino, Las Vegas, United States
Duration: 12 Dec 201614 Dec 2016
Conference number: 55


Conference55th IEEE Conference on Decision and Control (CDC 2016)
Abbreviated titleCDC02016
CountryUnited States
CityLas Vegas
Internet address

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