55 citations · 137 across the 38 of their papers we have counts for
37 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-…
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…
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…
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
Yuhan Liu, Máté Kiss, Roland Tóth +1
While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems where robustness to extrapolation/interpolation errors…
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
G. J. E. van Otterdijk, S. Moradi, S. Weiland +3
Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on mo…
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
Yuhan Liu, Roland Tóth, Maarten Schoukens
Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical in…
Space-Filling Input Design for Nonlinear State-Space Identification
Máté Kiss, Roland Tóth, Maarten Schoukens
The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing e…
Baseline Results for Selected Nonlinear System Identification Benchmarks
Max D. Champneys, Gerben I. Beintema, Roland Tóth +2
Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting im…
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…
State Derivative Normalization for Continuous-Time Deep Neural Networks
Jonas Weigand, Gerben I. Beintema, Jonas Ulmen +4
The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper nor…
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the…
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Nick Jaensson, Roland Tóth +1
In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neura…
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach
Rishi Ramkannan, Gerben I. Beintema, Roland Tóth +1
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data. To achieve this, it combines the rolled-out nonlinear sta…
Computationally efficient predictive control based on ANN state-space models
Jan H. Hoekstra, Bence Cseppentő, Gerben I. Beintema +3
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting mode…
Finite Dimensional Koopman Form of Polynomial Nonlinear Systems
Lucian Cristian Iacob, Maarten Schoukens, Roland Tóth
The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-ca…
A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies
David van de Sanden, Maarten Schoukens, Mauro Salazar
Mobility systems often suffer from a high price of anarchy due to the uncontrolled behavior of selfish users. This may result in societal costs that are significantly higher compar…
Deep Subspace Encoders for Nonlinear System Identification
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Using Artificial Neural Networks (ANN) for nonlinear system identification has proven to be a promising approach, but despite of all recent research efforts, many practical and the…
Koopman Form of Nonlinear Systems with Inputs
Lucian Cristian Iacob, Roland Tóth, Maarten Schoukens
The Koopman framework proposes a linear representation of finite-dimensional nonlinear systems through a generally infinite-dimensional globally linear embedding. Originally, the K…