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Bayesian nonparametric identification of Wiener systems
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-2831-2909
Linkoping Univ, Div Stat & Machine Learning, Linkoping, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-9368-3079
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 108, article id UNSP 108480Article in journal (Refereed) Published
Abstract [en]

We propose a nonparametric approach for the identification of Wiener systems. We model the impulse response of the linear block and the static nonlinearity using Gaussian processes. The hyperparameters of the Gaussian processes are estimated using an iterative algorithm based on stochastic approximation expectation-maximization. In the iterations, we use elliptical slice sampling to approximate the posterior distribution of the impulse response and update the hyperparameter estimates. The same sampling is finally used to sample the posterior distribution and to compute point estimates. We compare the proposed approach with a parametric approach and a semi-parametric approach. In particular, we show that the proposed method has an advantage when a parametric model for the system is not readily available.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 108, article id UNSP 108480
Keywords [en]
Spline models, Nonparametric estimation, System identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-260160DOI: 10.1016/j.automatica.2019.06.032ISI: 000483631400016Scopus ID: 2-s2.0-85068777049OAI: oai:DiVA.org:kth-260160DiVA, id: diva2:1356185
Note

QC 20191001

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-10-01Bibliographically approved

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Risuleo, Riccardo SvenHjalmarsson, Håkan

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