A finite-sample generalization bound for stable LPV systems
Abstract
Abstract One of the main theoretical challenges in learning dynamical systems from data is providing upper bounds on the generalization error, that is, the difference between the expected prediction error and the empirical prediction error measured on some finite sample. In machine learning, a popular class of such bounds are the so-called Probably Approximately Correct (PAC) bounds. In this paper, we derive a PAC bound for stable continuous-time linear parameter-varying (LPV) systems. Our bound depends on a weighted $$\text { H}_{\text { 2}}$$ H 2 -like norm of the chosen class of the LPV systems, but does not depend on the time interval for which the signals are considered.
Fields of Study & Subjects
Control Systems and Identification
Advanced Control Systems Optimization
Fault Detection and Control Systems
Publication details
- Journal / source
- Mathematics of Control Signals and Systems
- Publisher
- Springer Science and Business Media LLC
- Volume
- 38
- Issue
- 2
- Pages
- 241–273
- Publication date
- 8 April 2026
- Language
- EN