3 citations · 3 across the 3 of their papers we have counts for
5 papers
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…
Nonparametric Control Koopman Operators
Petar Bevanda, Bas Driessen, Lucian Cristian Iacob +3
This paper presents a novel Koopman composition operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictiona…
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…
Orthogonal-by-construction augmentation of physics-based input-output models
Bendegúz M. Györök, Maarten Schoukens, Tamás Péni +1
This paper proposes a novel orthogonal-by-construction parametrization for augmenting physics-based input-output models with a learning component in an additive sense. The parametr…
Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems
Lucian Cristian Iacob, Roland Tóth, Maarten Schoukens
The challenge of finding exact and finite-dimensional Koopman embeddings of nonlinear systems has been largely circumvented by employing data-driven techniques to learn models of d…