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Outlier robust syst...
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Bottegal, GiulioKTH,Reglerteknik
(author)
Outlier robust system identification : A Bayesian kernel-based approach
- Article/chapterEnglish2014
Publisher, publication year, extent ...
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IFAC Papers Online,2014
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Numbers
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LIBRIS-ID:oai:DiVA.org:kth-175122
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175122URI
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https://doi.org/10.3182/20140824-6-ZA-1003.01587DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:kon swepub-publicationtype
Notes
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QC 20151202
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In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. To build robustness to outliers, we model the measurement noise as realizations of independent Laplacian random variables. The identification problem is cast in a Bayesian framework, and solved by a new Markov Chain Monte Carlo (MCMC) scheme. In particular, exploiting the representation of the Laplacian random variables as scale mixtures of Gaussians, we design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods.
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Added entries (persons, corporate bodies, meetings, titles ...)
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Aravkin, A. Y.
(author)
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Hjalmarsson, HåkanKTH,Reglerteknik(Swepub:kth)u10a8l40
(author)
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Pillonetto, G.
(author)
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KTHReglerteknik
(creator_code:org_t)
Related titles
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In:IFAC Proceedings Volumes (IFAC-PapersOnline): IFAC Papers Online, s. 1073-10789783902823625
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