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• W2967221339 abstract "A different route to identification of time-invariant linear systems has been recently proposed which does not require committing to a specific parametric model structure. Impulse responses are described in a nonparametric Bayesian framework as zero-mean Gaussian processes. Their covariances are given by the so-called stable spline kernels encoding information on regularity and BIBO stability. In this paper, we demonstrate that these kernels also lead to a new family of radial basis functions kernels suitable to model system components subject to disturbances given by filtered white noise. This novel class, in cooperation with the stable spline kernels, paves the way to a new approach to solve missing data problems in both discrete and continuous-time settings. Numerical experiments show that the new technique may return models more predictive than those obtained by standard parametric Prediction Error Methods, also when these latter exploit the full data set." @default.
• W2967221339 created "2019-08-22" @default.
• W2967221339 creator A5017270452 @default.
• W2967221339 creator A5060156794 @default.
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• W2967221339 date "2019-08-11" @default.
• W2967221339 modified "2023-09-23" @default.
• W2967221339 title "Stable spline identification of linear systems under missing data" @default.
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• W2967221339 hasPublicationYear "2019" @default.
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