55 citations · 137 across the 38 of their papers we have counts for
6 papers · 1 filter
Robust Space-Filling Input Design via Stochastic Optimization
Máté Kiss, Roland Tóth, Maarten Schoukens
The space-filling input design approach generates a so-called space-filling dataset in the feature space of the system model. The design method is applicable on a broad class of mo…
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-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…
Improved Initialization for Port-Hamiltonian Neural Network Models
G. J. E. van Otterdijk, S. Weiland, M. Schoukens
Port-Hamiltonian neural networks have shown promising results in the identification of nonlinear dynamics of complex systems, as their combination of physical principles with data-…