Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2025 American Control Conference, ACC 2025 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 160-165 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9798331569372 |
| ISBN (Print) | 979-8-3503-6761-4 |
| Publikationsstatus | Veröffentlicht - 8 Juli 2025 |
| Veranstaltung | 2025 American Control Conference, ACC 2025 - Sheraton Denver Downtown Hotel, Denver, USA / Vereinigte Staaten Dauer: 8 Juli 2025 → 10 Juli 2025 |
Publikationsreihe
| Name | Proceedings of the American Control Conference |
|---|---|
| ISSN (Print) | 0743-1619 |
Abstract
This paper is centered around the approximation of dynamical systems by means of Gaussian processes. To this end, trajectories of such systems must be collected to be used as training data. The measurements of these trajectories are typically noisy, which implies that both the regression inputs and outputs are corrupted by noise. However, most of the literature considers only noise in the regression outputs. In this paper, we show how to account for the noise in the regression inputs in an extended Gaussian process framework to approximate scalar and multidimensional systems. We demonstrate the potential of our framework by comparing it to different state-of-the-art methods in several simulation examples.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2025 American Control Conference, ACC 2025. Institute of Electrical and Electronics Engineers Inc., 2025. S. 160-165 (Proceedings of the American Control Conference).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Gaussian Processes with Noisy Regression Inputs for Dynamical Systems
AU - Wolff, Tobias M.
AU - Lopez, Victor G.
AU - Muller, Matthias A.
N1 - Publisher Copyright: © 2025 AACC.
PY - 2025/7/8
Y1 - 2025/7/8
N2 - This paper is centered around the approximation of dynamical systems by means of Gaussian processes. To this end, trajectories of such systems must be collected to be used as training data. The measurements of these trajectories are typically noisy, which implies that both the regression inputs and outputs are corrupted by noise. However, most of the literature considers only noise in the regression outputs. In this paper, we show how to account for the noise in the regression inputs in an extended Gaussian process framework to approximate scalar and multidimensional systems. We demonstrate the potential of our framework by comparing it to different state-of-the-art methods in several simulation examples.
AB - This paper is centered around the approximation of dynamical systems by means of Gaussian processes. To this end, trajectories of such systems must be collected to be used as training data. The measurements of these trajectories are typically noisy, which implies that both the regression inputs and outputs are corrupted by noise. However, most of the literature considers only noise in the regression outputs. In this paper, we show how to account for the noise in the regression inputs in an extended Gaussian process framework to approximate scalar and multidimensional systems. We demonstrate the potential of our framework by comparing it to different state-of-the-art methods in several simulation examples.
UR - http://www.scopus.com/inward/record.url?scp=105015840209&partnerID=8YFLogxK
U2 - 10.23919/ACC63710.2025.11107496
DO - 10.23919/ACC63710.2025.11107496
M3 - Conference contribution
AN - SCOPUS:105015840209
SN - 979-8-3503-6761-4
T3 - Proceedings of the American Control Conference
SP - 160
EP - 165
BT - 2025 American Control Conference, ACC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 American Control Conference, ACC 2025
Y2 - 8 July 2025 through 10 July 2025
ER -