Maarten Schoukens

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 75

Top Topics

Impulse Response

Predictive Control

Tree Adjoining Grammar

Top Venues

Publications

Max D. Champneys, Gerben Izaak Beintema, Roland Tóth, Maarten Schoukens, Timothy J. Rogers
Baseline Results for Selected Nonlinear System Identification Benchmarks. CoRR (2024)
Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth
Meta-state-space learning: An identification approach for stochastic dynamical systems. Autom. 167 (2024)
Thomas de Jong, Valentina Breschi, Maarten Schoukens, Mircea Lazar
Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees. CoRR (2024)
Jan H. Hoekstra, Chris Verhoek, Roland Tóth, Maarten Schoukens
Learning-based model augmentation with LFRs. CoRR (2024)
Lucian-Cristian Iacob, Roland Tóth, Maarten Schoukens
Koopman form of nonlinear systems with inputs. Autom. 162 (2024)
Dilan Öztürk, Pepijn Saraber, Kevin Bielawski, Alessandro Giudici, Leon Schurgers, Koen D. Reesink, Maarten Schoukens
Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics. MeMeA (2024)
Jonas Weigand, Gerben Izaak Beintema, Jonas Ulmen, Daniel Görges, Roland Tóth, Maarten Schoukens, Martin Ruskowski
State Derivative Normalization for Continuous-Time Deep Neural Networks. CoRR (2024)
Yuhan Liu, Roland Tóth, Maarten Schoukens
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks. CoRR (2024)
Máté Kiss, Roland Tóth, Maarten Schoukens
Space-Filling Input Design for Nonlinear State-Space Identification. CoRR (2024)
T. O. de Jong, Valentina Breschi, Maarten Schoukens, Simone Formentin
Data-Driven Model-Reference Control With Closed-Loop Stability: The Output-Feedback Case. IEEE Control. Syst. Lett. 7 (2023)
Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth
Deep subspace encoders for nonlinear system identification. Autom. 156 (2023)
Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth
Continuous-time identification of dynamic state-space models by deep subspace encoding. ICLR (2023)
Jan H. Hoekstra, Bence Cseppento, Gerben Izaak Beintema, Maarten Schoukens, Zsolt Kollár, Roland Tóth
Computationally Efficient Predictive Control Based on ANN State-Space Models. CDC (2023)
Rishi Ramkannan, Gerben Izaak Beintema, Roland Tóth, Maarten Schoukens
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach. CoRR (2023)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
Automated multi-objective system identification using grammar-based genetic programming. Autom. 154 (2023)
Sarvin Moradi, Nick Jaensson, Roland Tóth, Maarten Schoukens
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models. CoRR (2023)
Lucian-Cristian Iacob, Maarten Schoukens, Roland Tóth
Finite Dimensional Koopman Form of Polynomial Nonlinear Systems. CoRR (2023)
Jan H. Hoekstra, Bence Cseppento, Gerben Izaak Beintema, Maarten Schoukens, Zsolt Kollár, Roland Tóth
Computationally efficient predictive control based on ANN state-space models. CoRR (2023)
Mircea Lazar, Mihai-Serban Popescu, Maarten Schoukens
Nonlinear Data-Driven Predictive Control Using Deep Subspace Prediction Networks. CDC (2023)
Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems. CoRR (2023)
L. H. Peeters, Gerben Izaak Beintema, M. Forgione, Maarten Schoukens
NARX Identification using Derivative-Based Regularized Neural Networks. CDC (2022)
Chris Verhoek, Gerben Izaak Beintema, Sofie Haesaert, Maarten Schoukens, Roland Tóth
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems. CoRR (2022)
Albert Podusenko, Semih Akbayrak, Ismail Senöz, Maarten Schoukens, Wouter M. Kouw
Message Passing-based System Identification for NARMAX Models. CDC (2022)
Wouter M. Kouw, Albert Podusenko, Magnus T. Koudahl, Maarten Schoukens
Variational message passing for online polynomial NARMAX identification. ACC (2022)
L. H. Peeters, Gerben Izaak Beintema, M. Forgione, Maarten Schoukens
NARX Identification using Derivative-Based Regularized Neural Networks. CoRR (2022)
Wouter M. Kouw, Albert Podusenko, Magnus T. Koudahl, Maarten Schoukens
Variational message passing for online polynomial NARMAX identification. CoRR (2022)
Lucian-Cristian Iacob, Roland Tóth, Maarten Schoukens
Koopman Form of Nonlinear Systems with Inputs. CoRR (2022)
Chris Verhoek, Gerben Izaak Beintema, Sofie Haesaert, Maarten Schoukens, Roland Tóth
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems. CDC (2022)
Lucian-Cristian Iacob, Roland Tóth, Maarten Schoukens
Optimal Synthesis of LTI Koopman Models for Nonlinear Systems with Inputs. CoRR (2022)
Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth
Deep Subspace Encoders for Nonlinear System Identification. CoRR (2022)
Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth
Deep subspace encoders for continuous-time state-space identification. CoRR (2022)
David van de Sanden, Maarten Schoukens, Mauro Salazar
A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies. CoRR (2022)
Lucian-Cristian Iacob, Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth
Deep Identification of Nonlinear Systems in Koopman Form. CoRR (2021)
Maarten Schoukens
Improved Initialization of State-Space Artificial Neural Networks. ECC (2021)
Maarten Schoukens
Improved Initialization of State-Space Artificial Neural Networks. CoRR (2021)
Gerben Beintema, Roland Tóth, Maarten Schoukens
Nonlinear state-space identification using deep encoder networks. L4DC (2021)
Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
Feedback identification of conductance-based models. Autom. 123 (2021)
Gábor Rödönyi, Gerben Izaak Beintema, Roland Tóth, Maarten Schoukens, Daniel Pup, Ádám Kisari, Zs. Vígh, Peter Korös, Alexandros Soumelidis, József Bokor
Identification of the nonlinear steering dynamics of an autonomous vehicle. CoRR (2021)
Lucian-Cristian Iacob, Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth
Deep Identification of Nonlinear Systems in Koopman Form. CDC (2021)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
A Tree Adjoining Grammar representation for models of stochastic dynamical systems. Autom. 119 (2020)
Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
Feedback Identification of conductance-based models. CoRR (2020)
Maarten Schoukens, Roland Tóth
On the Initialization of Nonlinear LFR Model Identification with the Best Linear Approximation. CoRR (2020)
Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
System identification of biophysical neuronal models. CDC (2020)
Gerben Beintema, Roland Tóth, Maarten Schoukens
Nonlinear state-space identification using deep encoder networks. CoRR (2020)
Rik Pintelon, Maarten Schoukens, John Lataire
Best Linear Approximation of Nonlinear Continuous-Time Systems Subject to Process Noise and Operating in Feedback. CoRR (2020)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
A Tree Adjoining Grammar Representation for Models Of Stochastic Dynamical Systems. CoRR (2020)
Rik Pintelon, Maarten Schoukens, John Lataire
Best Linear Approximation of Nonlinear Continuous-Time Systems Subject to Process Noise and Operating in Feedback. IEEE Trans. Instrum. Meas. 69 (10) (2020)
Stefan-Cristian Nechita, Roland Tóth, Dhruv Khandelwal, Maarten Schoukens
Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming. CoRR (2020)
Maarten Schoukens, Rik Pintelon, Tadeusz P. Dobrowiecki, Johan Schoukens
Extending the Best Linear Approximation Framework to the Process Noise Case. IEEE Trans. Autom. Control. 65 (4) (2020)
Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
System identification of biophysical neuronal models. CoRR (2020)
Gerben Izaak Beintema, Roland Tóth, Maarten Schoukens
Non-linear State-space Model Identification from Video Data using Deep Encoders. CoRR (2020)
Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
Feedback for nonlinear system identification. CoRR (2020)
Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre
Feedback for nonlinear system identification. ECC (2019)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic Programming. CEC (2019)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic Programming. CoRR (2019)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
Grammar-based Representation and Identification of Dynamical Systems. ECC (2019)
Tarek Ahmed-Ali, Koen Tiels, Maarten Schoukens, Fouad Giri
Sampled-data adaptive observer for state-affine systems with uncertain output equation. Autom. 103 (2019)
Maarten Schoukens, Roland Tóth
From Nonlinear Identification to Linear Parameter Varying Models: Benchmark Examples. CoRR (2018)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
On the Simulation of Polynomial NARMAX Models. CDC (2018)
Maarten Schoukens, Rik Pintelon, Tadeusz P. Dobrowiecki, Johan Schoukens
Extending the Best Linear Approximation Framework to the Process Noise Case. CoRR (2018)
Johan Schoukens, Rik Pintelon, Yves Rolain, Maarten Schoukens, Koen Tiels, Laurent Vanbeylen, Anne Van Mulders, Gerd Vandersteen
Structure Discrimination in Block-Oriented Models Using Linear Approximations: A Theoretic Framework. CoRR (2018)
Anna Marconato, Maarten Schoukens, Koen Tiels, Widanalage Dhammika Widanage, Amjad Abu-Rmileh, Johan Schoukens
Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example. CoRR (2018)
Maarten Schoukens, Gerd Vandersteen, Yves Rolain, Francesco Ferranti
Fast Identification of Wiener-Hammerstein Systems using Discrete Optimization. CoRR (2018)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
Grammar-based Representation and Identification of Dynamical Systems. CoRR (2018)
Erliang Zhang, Maarten Schoukens, Johan Schoukens
Structure detection of Wiener-Hammerstein systems with process noise. CoRR (2018)
Maarten Schoukens, Roland Tóth
Linear Parameter Varying Representation of a class of MIMO Nonlinear Systems. CoRR (2018)
Dhruv Khandelwal, Maarten Schoukens, Roland Tóth
On the Simulation of Polynomial NARMAX Models. CoRR (2018)
Maarten Schoukens, Koen Tiels
Identification of block-oriented nonlinear systems starting from linear approximations: A survey. Autom. 85 (2017)
Erliang Zhang, Maarten Schoukens, Johan Schoukens
Structure Detection of Wiener-Hammerstein Systems With Process Noise. IEEE Trans. Instrum. Meas. 66 (3) (2017)
Maarten Schoukens, Anna Marconato, Rik Pintelon, Gerd Vandersteen, Yves Rolain
Parametric identification of parallel Wiener-Hammerstein systems. CoRR (2017)
Koen Tiels, Maarten Schoukens, Johan Schoukens
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines. CoRR (2016)
Maarten Schoukens, Jules Hammenecker, Adam Cooman
Obtaining the Pre-Inverse of a Power Amplifier using Iterative Learning Control. CoRR (2016)
Anna Marconato, Maarten Schoukens, Johan Schoukens
Filter-based regularisation for impulse response modelling. CoRR (2016)
Maarten Schoukens, Koen Tiels
Identification of Nonlinear Block-Oriented Systems starting from Linear Approximations: A Survey. CoRR (2016)
Anna Marconato, Maarten Schoukens, Johan Schoukens
Filter interpretation of regularized impulse response modeling. ECC (2016)
Philippe Dreesen, Maarten Schoukens, Koen Tiels, Johan Schoukens
Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach. I2MTC (2015)
Koen Tiels, Maarten Schoukens, Johan Schoukens
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines. Autom. 60 (2015)
Johan Schoukens, Rik Pintelon, Yves Rolain, Maarten Schoukens, Koen Tiels, Laurent Vanbeylen, Anne Van Mulders, Gerd Vandersteen
Structure discrimination in block-oriented models using linear approximations: A theoretic framework. Autom. 53 (2015)
Maarten Schoukens, Anna Marconato, Rik Pintelon, Gerd Vandersteen, Yves Rolain
Parametric identification of parallel Wiener-Hammerstein systems. Autom. 51 (2015)
Maarten Schoukens, Rik Pintelon, Yves Rolain
Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation. Autom. 50 (2) (2014)
Johan Schoukens, Anna Marconato, Rik Pintelon, Yves Rolain, Maarten Schoukens, Koen Tiels, Laurent Vanbeylen, Gerd Vandersteen, Anne Van Mulders
System identification in a real world. AMC (2014)
Maarten Schoukens, Christian Lyzell, Martin Enqvist
Combining the Best Linear Approximation and Dimension Reduction to Identify the Linear Blocks of Parallel Wiener Systems. ALCOSP (2013)
Maarten Schoukens, Gerd Vandersteen, Yves Rolain
An identification algorithm for parallel Wiener-Hammerstein systems. CDC (2013)
Anna Marconato, Maarten Schoukens, Yves Rolain, Johan Schoukens
Study of the effective number of parameters in nonlinear identification benchmarks. CDC (2013)
Maarten Schoukens, Yves Rolain
Cross-term Elimination in Parallel Wiener Systems Using a Linear Input Transformation. IEEE Trans. Instrum. Meas. 61 (3) (2012)
Maarten Schoukens, Yves Rolain
Parametric Identification of Parallel Wiener Systems. IEEE Trans. Instrum. Meas. 61 (10) (2012)
Maarten Schoukens, Rik Pintelon, Yves Rolain
Parametric Identification of Parallel Hammerstein Systems. IEEE Trans. Instrum. Meas. 60 (12) (2011)
Maarten Schoukens, Yves Rolain
Parametric MIMO parallel Wiener identification. CDC/ECC (2011)