Koen Tiels

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 42

Top Topics

Recurrent Networks

Initial Estimates

Orthonormal Basis

Multivariate Polynomials

Top Venues

IEEE Control. Syst. Lett.

Publications

Rodrigo A. González, Koen Tiels, Tom Oomen
Kernel-based identification using Lebesgue-sampled data. Autom. 164 (2024)
Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova
Magnetic Hysteresis Modeling with Neural Operators. CoRR (2024)
Nic Dirkx, Koen Tiels, Tom Oomen
A Wavelet-Based Approach to FRF Identification From Incomplete Data. IEEE Trans. Instrum. Meas. 72 (2023)
Pawel Wachel, Koen Tiels, Maciej Filinski
Learning low-dimensional separable decompositions of MIMO non-linear systems. Int. J. Control 96 (4) (2023)
Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, Pawel Wachel
A Computationally Lightweight Safe Learning Algorithm. CDC (2023)
Rodrigo A. González, Koen Tiels, Tom Oomen
Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach. CoRR (2023)
Abhishek Chandra, Taniya Kapoor, Bram Daniels, Mitrofan Curti, Koen Tiels, Daniel M. Tartakovsky, Elena A. Lomonova
Neural oscillators for magnetic hysteresis modeling. CoRR (2023)
Nard Strijbosch, Koen Tiels, Tom Oomen
Memory-Element-Based Hysteresis: Identification and Compensation of a Piezoelectric Actuator. IEEE Trans. Control. Syst. Technol. 31 (6) (2023)
Rodrigo A. González, Koen Tiels, Tom Oomen
Kernel-based identification using Lebesgue-sampled data. CoRR (2023)
Nic Dirkx, Koen Tiels, Tom Oomen
Iterative Robust Experiment Design for MIMO System Identification via the S-Lemma. CCTA (2023)
Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky
Discovering Sparse Hysteresis Models: A Data-driven Study for Piezoelectric Materials and Perspectives on Magnetic Hysteresis. CoRR (2023)
Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, Pawel Wachel
A computationally lightweight safe learning algorithm. CoRR (2023)
Jan Decuyper, Koen Tiels, Siep Weiland, Mark C. Runacres, Johan Schoukens
Decoupling multivariate functions using a nonparametric filtered tensor decomposition. CoRR (2022)
Nard Strijbosch, Koen Tiels, Tom Oomen
Hysteresis Feedforward Compensation: A Direct Tuning Approach Using Hybrid-MEM-Elements. IEEE Control. Syst. Lett. 6 (2022)
Abhishek Chandra, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky
Physics-informed neural networks for modelling anisotropic and bi-anisotropic electromagnetic constitutive laws through indirect data. SSCI (2022)
Jan Decuyper, Koen Tiels, Johan Schoukens
PNLSS Toolbox 1.0. CoRR (2021)
Jan Decuyper, Koen Tiels, Siep Weiland, Johan Schoukens
Decoupling multivariate functions using a non-parametric Filtered CPD approach. CoRR (2021)
Maciej Filinski, Pawel Wachel, Koen Tiels
Low-Dimensional Decompositions for Nonlinear Finite Impulse Response Modeling. ICCS (5) (2021)
Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness. AISTATS (2020)
Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis Antonio Aguirre
On the smoothness of nonlinear system identification. Autom. 121 (2020)
Jan Decuyper, Koen Tiels, Mark C. Runacres, Johan Schoukens
Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling. CoRR (2020)
Jan Decuyper, Tim De Troyer, Koen Tiels, Johan Schoukens, Mark C. Runacres
A nonlinear model of vortex-induced forces on an oscillating cylinder in a fluid flow. CoRR (2020)
Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön
The trade-off between long-term memory and smoothness for recurrent networks. CoRR (2019)
Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis Antonio Aguirre
On the Smoothness of Nonlinear System Identification. CoRR (2019)
Carl R. Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön
Deep Convolutional Networks in System Identification. CoRR (2019)
Carl R. Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön
Deep Convolutional Networks in System Identification. CDC (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)
Jan Decuyper, Philippe Dreesen, Johan Schoukens, Mark Runacres, Koen Tiels
Decoupling Multivariate Polynomials for Nonlinear State-Space Models. IEEE Control. Syst. Lett. 3 (3) (2019)
Rishi Relan, Koen Tiels, Anna Marconato, Philippe Dreesen, Johan Schoukens
Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record. 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)
Rishi Relan, Koen Tiels, Jean-Marc Timmermans, Johan Schoukens
A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets. CoRR (2018)
Jan Decuyper, Tim De Troyer, Mark Runacres, Koen Tiels, Johan Schoukens
Nonlinear state-space modelling of the kinematics of an oscillating circular cylinder in a fluid flow. CoRR (2018)
Ricardo Castro-Garcia, Koen Tiels, Oscar Mauricio Agudelo, Johan A. K. Suykens
Hammerstein system identification through best linear approximation inversion and regularisation. Int. J. Control 91 (8) (2018)
Maarten Schoukens, Koen Tiels
Identification of block-oriented nonlinear systems starting from linear approximations: A survey. Autom. 85 (2017)
Alireza Fakhrizadeh Esfahani, Philippe Dreesen, Koen Tiels, Jean-Philippe Noël, Johan Schoukens
Parameter reduction in nonlinear state-space identification of hysteresis. CoRR (2017)
Philippe Dreesen, Koen Tiels, Mariya Ishteva, Johan Schoukens
Nonlinear system identification: Finding structure in nonlinear black-box models. CAMSAP (2017)
Rishi Relan, Koen Tiels, Jean-Marc Timmermans, Johan Schoukens
A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets. IEEE Control. Syst. Lett. 1 (1) (2017)
Koen Tiels, Maarten Schoukens, Johan Schoukens
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines. CoRR (2016)
Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Koen Tiels, Johan A. K. Suykens
Hammerstein system identification using LS-SVM and steady state time response. ECC (2016)
Maarten Schoukens, Koen Tiels
Identification of Nonlinear Block-Oriented Systems starting from Linear Approximations: A Survey. CoRR (2016)
Koen Tiels, Johan Schoukens
Wiener system identification with generalized orthonormal basis functions. CoRR (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)
Ricardo Castro-Garcia, Koen Tiels, Johan Schoukens, Johan A. K. Suykens
Incorporating Best Linear Approximation within LS-SVM-based Hammerstein System Identification. CDC (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)
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)
Koen Tiels, Johan Schoukens
Wiener system identification with generalized orthonormal basis functions. Autom. 50 (12) (2014)
Koen Tiels, Johan Schoukens
From coupled to decoupled polynomial representations in parallel Wiener-Hammerstein models. CDC (2013)
Koen Tiels, Johan Schoukens
Iterative Update of the Pole Locations in a Wiener-Schetzen Model. ALCOSP (2013)
Koen Tiels, Johan Schoukens
Identifying a Wiener system using a variant of the Wiener G-Functionals. CDC/ECC (2011)