6 papers
Scalable Bi-causal Optimal Transport via KL Relaxation and Policy Gradients
Haoyang Cao, Jesse Hoekstra, Renyuan Xu +2
Bi-causal optimal transport (OT) is a natural framework for comparing and coupling stochastic processes under nonanticipative information constraints, with important applications i…
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…
Orthogonal projection-based regularization for efficient model augmentation
Bendegúz M. Györök, Jan H. Hoekstra, Johan Kon +3
Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical…
Learning-based model augmentation with LFRs
Jan H. Hoekstra, Chris Verhoek, Roland Tóth +1
Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. In particular, recent encoder-based methods for artifici…