Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.automatica.2019.108493 | Automatica |
Keywords | Field | DocType |
Linear system identification,Missing data,Gaussian processes,Kernel-based regularization,Stable spline kernels,Radial basis functions kernels,Stable spline imputation | Spline (mathematics),Mathematical optimization,Parametric model,Linear system,Algorithm,White noise,BIBO stability,Parametric statistics,Gaussian process,Missing data,Mathematics | Journal |
Volume | Issue | ISSN |
108 | 1 | 0005-1098 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pillonetto Gianluigi | 1 | 877 | 80.84 |
| Alessandro Chiuso | 2 | 1159 | 103.17 |
| Giuseppe De Nicolao | 3 | 738 | 76.26 |