13 papers
Extreme points of sets of probability measures and -divergences
Gerrit Bauch, Max Nendel, Alessandro Sgarabottolo
In this work, we prove several equivalent characterizations of the extreme points of convex sets of probability measures of the form , where $\mathc…
Online learning of neural state-space models
Bendegúz Györök, Tamás Péni, Maarten Schoukens +1
Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) models that achieve state-of-the-art perf…
Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
Roel Drenth, Jan H. Hoekstra, Maarten Schoukens +1
Identifying control-friendly models of nonlinear systems remains one of the major challenges at the intersection of system identification and control. The Linear Parameter-Varying…
Learning Koopman Models From Data Under General Noise Conditions
Lucian Cristian Iacob, Máté Szécsi, Gerben Izaak Beintema +2
This paper presents a novel identification approach of Koopman models of nonlinear systems with inputs under rather general noise conditions. The method uses deep state-space encod…
Learning-based augmentation of first-principle models: A linear fractional representation-based approach
Jan H. Hoekstra, Bendegúz M. Györök, Roland Tóth +1
Nonlinear system identificationhas proven to be effective in obtaining accurate models from data for complex real-world systems. In particular, recent encoder-based methods with ar…
Encoder initialisation methods in the model augmentation setting
J. H. Hoekstra, B. Györök, R. Töth +1
Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. Recent encoder-based methods for artificial neural netwo…