Linear time-varying systems are a class of systems, the dynamics of which evolve in time. This results in a time-varying frequency response function where each frequency has a time-varying gain. In classical identification techniques, basis functions are employed to fit these time-varying gains. In this paper a new method based on Gaussian process regression is presented. The advantage of the proposed method is a more convenient model structure and model order selection.
|Title of host publication||IFAC-PapersOnLine|
|Editors||Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann|
|Number of pages||6|
|Publication status||Published - 1 Jan 2020|
|Event||21st IFAC World Congress : Automatic Control – Meeting Societal Challenges - virtual IFAC World Congress, Berlin, Germany|
Duration: 11 Jul 2020 → 17 Jul 2020
|Conference||21st IFAC World Congress|
|Period||11/07/20 → 17/07/20|
Bibliographical noteFunding Information:
This research was supported in part by the Fund for Scientific Research (FWO Vlaanderen), and in part by the Flemish Government (Methusalem Grant METH1).
Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license
Copyright 2022 Elsevier B.V., All rights reserved.
- Linear time-varying systems
- Gaussian processes
- Machine learning