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2020 – today
- 2024
[j14]Rodrigo A. González
, Koen Tiels, Tom Oomen:
Kernel-based identification using Lebesgue-sampled data. Autom. 164: 111648 (2024)- [i23]Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova:
Magnetic Hysteresis Modeling with Neural Operators. CoRR abs/2407.03261 (2024) - 2023
- [j13]Pawel Wachel, Koen Tiels, Maciej Filinski:
Learning low-dimensional separable decompositions of MIMO non-linear systems. Int. J. Control 96(4): 900-906 (2023) - [j12]Nard Strijbosch, Koen Tiels, Tom Oomen:
Memory-Element-Based Hysteresis: Identification and Compensation of a Piezoelectric Actuator. IEEE Trans. Control. Syst. Technol. 31(6): 2863-2870 (2023) - [j11]Nic Dirkx, Koen Tiels, Tom Oomen:
A Wavelet-Based Approach to FRF Identification From Incomplete Data. IEEE Trans. Instrum. Meas. 72: 1-15 (2023) - [c14]Nic Dirkx, Koen Tiels, Tom Oomen:
Iterative Robust Experiment Design for MIMO System Identification via the S-Lemma. CCTA 2023: 998-1003 - [c13]Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, Pawel Wachel:
A Computationally Lightweight Safe Learning Algorithm. CDC 2023: 1022-1027 - [i22]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 abs/2302.05313 (2023) - [i21]Rodrigo A. González, Koen Tiels, Tom Oomen:
Kernel-based identification using Lebesgue-sampled data. CoRR abs/2303.06045 (2023) - [i20]Rodrigo A. González, Koen Tiels, Tom Oomen:
Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach. CoRR abs/2304.03312 (2023) - [i19]Abhishek Chandra, Taniya Kapoor, Bram Daniels, Mitrofan Curti, Koen Tiels, Daniel M. Tartakovsky, Elena A. Lomonova:
Neural oscillators for magnetic hysteresis modeling. CoRR abs/2308.12002 (2023) - [i18]Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, Pawel Wachel:
A computationally lightweight safe learning algorithm. CoRR abs/2309.03672 (2023) - 2022
- [j10]Nard Strijbosch, Koen Tiels, Tom Oomen:
Hysteresis Feedforward Compensation: A Direct Tuning Approach Using Hybrid-MEM-Elements. IEEE Control. Syst. Lett. 6: 1070-1075 (2022) - [c12]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: 1451-1459 - [i17]Jan Decuyper, Koen Tiels, Siep Weiland, Mark C. Runacres, Johan Schoukens:
Decoupling multivariate functions using a nonparametric filtered tensor decomposition. CoRR abs/2205.11153 (2022) - 2021
- [c11]Maciej Filinski, Pawel Wachel, Koen Tiels:
Low-Dimensional Decompositions for Nonlinear Finite Impulse Response Modeling. ICCS (5) 2021: 352-359 - [i16]Jan Decuyper, Koen Tiels, Siep Weiland, Johan Schoukens:
Decoupling multivariate functions using a non-parametric Filtered CPD approach. CoRR abs/2105.08518 (2021) - [i15]Jan Decuyper, Koen Tiels, Johan Schoukens:
PNLSS Toolbox 1.0. CoRR abs/2105.08534 (2021) - 2020
- [j9]Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis Antonio Aguirre:
On the smoothness of nonlinear system identification. Autom. 121: 109158 (2020) - [c10]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: 2370-2380 - [i14]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 abs/2001.02861 (2020) - [i13]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 abs/2006.05932 (2020)
2010 – 2019
- 2019
- [j8]Tarek Ahmed-Ali, Koen Tiels, Maarten Schoukens, Fouad Giri:
Sampled-data adaptive observer for state-affine systems with uncertain output equation. Autom. 103: 96-105 (2019) - [j7]Jan Decuyper, Philippe Dreesen, Johan Schoukens, Mark Runacres, Koen Tiels:
Decoupling Multivariate Polynomials for Nonlinear State-Space Models. IEEE Control. Syst. Lett. 3(3): 745-750 (2019) - [c9]Carl R. Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön:
Deep Convolutional Networks in System Identification. CDC 2019: 3670-3676 - [i12]Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis Antonio Aguirre:
On the Smoothness of Nonlinear System Identification. CoRR abs/1905.00820 (2019) - [i11]Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön:
The trade-off between long-term memory and smoothness for recurrent networks. CoRR abs/1906.08482 (2019) - [i10]Carl R. Andersson, Antônio H. Ribeiro, Koen Tiels, Niklas Wahlström, Thomas B. Schön:
Deep Convolutional Networks in System Identification. CoRR abs/1909.01730 (2019) - 2018
- [j6]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): 1757-1773 (2018) - [i9]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 abs/1804.07035 (2018) - [i8]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 abs/1804.08383 (2018) - [i7]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 abs/1804.09648 (2018) - [i6]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 abs/1805.06237 (2018) - [i5]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 abs/1805.06252 (2018) - 2017
- [j5]Maarten Schoukens, Koen Tiels:
Identification of block-oriented nonlinear systems starting from linear approximations: A survey. Autom. 85: 272-292 (2017) - [j4]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): 182-187 (2017) - [c8]Philippe Dreesen, Koen Tiels, Mariya Ishteva, Johan Schoukens:
Nonlinear system identification: Finding structure in nonlinear black-box models. CAMSAP 2017: 1-4 - [i4]Alireza Fakhrizadeh Esfahani, Philippe Dreesen, Koen Tiels, Jean-Philippe Noël, Johan Schoukens:
Parameter reduction in nonlinear state-space identification of hysteresis. CoRR abs/1705.00178 (2017) - 2016
- [c7]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: 1063-1068 - [i3]Maarten Schoukens, Koen Tiels:
Identification of Nonlinear Block-Oriented Systems starting from Linear Approximations: A Survey. CoRR abs/1607.01217 (2016) - [i2]Koen Tiels, Johan Schoukens:
Wiener system identification with generalized orthonormal basis functions. CoRR abs/1612.04558 (2016) - [i1]Koen Tiels, Maarten Schoukens, Johan Schoukens:
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines. CoRR abs/1612.04568 (2016) - 2015
- [j3]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: 225-234 (2015) - [j2]Koen Tiels, Maarten Schoukens, Johan Schoukens:
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines. Autom. 60: 201-209 (2015) - [c6]Ricardo Castro-Garcia, Koen Tiels, Johan Schoukens, Johan A. K. Suykens:
Incorporating Best Linear Approximation within LS-SVM-based Hammerstein System Identification. CDC 2015: 7392-7397 - [c5]Philippe Dreesen, Maarten Schoukens, Koen Tiels, Johan Schoukens:
Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach. I2MTC 2015: 987-992 - 2014
- [j1]Koen Tiels, Johan Schoukens:
Wiener system identification with generalized orthonormal basis functions. Autom. 50(12): 3147-3154 (2014) - [c4]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: 1-9 - 2013
- [c3]Koen Tiels, Johan Schoukens:
Iterative Update of the Pole Locations in a Wiener-Schetzen Model. ALCOSP 2013: 366-371 - [c2]Koen Tiels, Johan Schoukens:
From coupled to decoupled polynomial representations in parallel Wiener-Hammerstein models. CDC 2013: 4937-4942 - 2011
- [c1]Koen Tiels, Johan Schoukens:
Identifying a Wiener system using a variant of the Wiener G-Functionals. CDC/ECC 2011: 5780-5785
Coauthor Index
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