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ORCIDMaarten Schoukens
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2020 – today
- 2021
[j14]Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre:
Feedback identification of conductance-based models. Autom. 123: 109297 (2021)- [i25]Maarten Schoukens:
Improved Initialization of State-Space Artificial Neural Networks. CoRR abs/2103.14516 (2021) - 2020
- [j13]Dhruv Khandelwal, Maarten Schoukens
, Roland Tóth:
A Tree Adjoining Grammar representation for models of stochastic dynamical systems. Autom. 119: 109099 (2020) - [j12]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): 1514-1524 (2020) - [j11]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): 8600-8612 (2020) - [c12]Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre:
System identification of biophysical neuronal models. CDC 2020: 6180-6185 - [i24]Dhruv Khandelwal, Maarten Schoukens, Roland Tóth:
A Tree Adjoining Grammar Representation for Models Of Stochastic Dynamical Systems. CoRR abs/2001.05320 (2020) - [i23]Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre:
Feedback Identification of conductance-based models. CoRR abs/2002.09626 (2020) - [i22]Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre:
Feedback for nonlinear system identification. CoRR abs/2002.09627 (2020) - [i21]Rik Pintelon, Maarten Schoukens, John Lataire:
Best Linear Approximation of Nonlinear Continuous-Time Systems Subject to Process Noise and Operating in Feedback. CoRR abs/2004.02579 (2020) - [i20]Maarten Schoukens, Roland Tóth:
On the Initialization of Nonlinear LFR Model Identification with the Best Linear Approximation. CoRR abs/2004.05040 (2020) - [i19]Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre:
System identification of biophysical neuronal models. CoRR abs/2012.07691 (2020) - [i18]Gerben Beintema, Roland Tóth, Maarten Schoukens:
Nonlinear state-space identification using deep encoder networks. CoRR abs/2012.07697 (2020) - [i17]Gerben Izaak Beintema, Roland Tóth, Maarten Schoukens:
Non-linear State-space Model Identification from Video Data using Deep Encoders. CoRR abs/2012.07721 (2020) - [i16]Stefan-Cristian Nechita, Roland Tóth, Dhruv Khandelwal, Maarten Schoukens:
Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming. CoRR abs/2012.08834 (2020)
2010 – 2019
- 2019
- [j10]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) - [c11]Dhruv Khandelwal, Maarten Schoukens, Roland Tóth:
Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic Programming. CEC 2019: 2673-2680 - [c10]Dhruv Khandelwal, Maarten Schoukens, Roland Tóth:
Grammar-based Representation and Identification of Dynamical Systems. ECC 2019: 1318-1323 - [c9]Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre:
Feedback for nonlinear system identification. ECC 2019: 1344-1349 - [i15]Dhruv Khandelwal, Maarten Schoukens, Roland Tóth:
Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic Programming. CoRR abs/1904.03152 (2019) - 2018
- [c8]Dhruv Khandelwal, Maarten Schoukens, Roland Tóth:
On the Simulation of Polynomial NARMAX Models. CDC 2018: 1445-1450 - [i14]Maarten Schoukens, Gerd Vandersteen, Yves Rolain, Francesco Ferranti:
Fast Identification of Wiener-Hammerstein Systems using Discrete Optimization. CoRR abs/1804.07034 (2018) - [i13]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) - [i12]Maarten Schoukens, Rik Pintelon, Tadeusz P. Dobrowiecki, Johan Schoukens:
Extending the Best Linear Approximation Framework to the Process Noise Case. CoRR abs/1804.07510 (2018) - [i11]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) - [i10]Erliang Zhang, Maarten Schoukens, Johan Schoukens:
Structure detection of Wiener-Hammerstein systems with process noise. CoRR abs/1804.10022 (2018) - [i9]Maarten Schoukens, Roland Tóth:
From Nonlinear Identification to Linear Parameter Varying Models: Benchmark Examples. CoRR abs/1809.05000 (2018) - [i8]Maarten Schoukens, Roland Tóth:
Linear Parameter Varying Representation of a class of MIMO Nonlinear Systems. CoRR abs/1809.05011 (2018) - [i7]Dhruv Khandelwal, Maarten Schoukens, Roland Tóth:
On the Simulation of Polynomial NARMAX Models. CoRR abs/1810.06883 (2018) - [i6]Dhruv Khandelwal, Maarten Schoukens, Roland Tóth:
Grammar-based Representation and Identification of Dynamical Systems. CoRR abs/1811.10576 (2018) - 2017
- [j9]Maarten Schoukens, Koen Tiels:
Identification of block-oriented nonlinear systems starting from linear approximations: A survey. Autom. 85: 272-292 (2017) - [j8]Erliang Zhang, Maarten Schoukens, Johan Schoukens:
Structure Detection of Wiener-Hammerstein Systems With Process Noise. IEEE Trans. Instrum. Meas. 66(3): 569-576 (2017) - [i5]Maarten Schoukens, Anna Marconato, Rik Pintelon, Gerd Vandersteen, Yves Rolain:
Parametric identification of parallel Wiener-Hammerstein systems. CoRR abs/1708.06543 (2017) - 2016
- [c7]Anna Marconato, Maarten Schoukens, Johan Schoukens:
Filter interpretation of regularized impulse response modeling. ECC 2016: 1655-1660 - [i4]Maarten Schoukens, Jules Hammenecker, Adam Cooman:
Obtaining the Pre-Inverse of a Power Amplifier using Iterative Learning Control. CoRR abs/1606.08663 (2016) - [i3]Maarten Schoukens, Koen Tiels:
Identification of Nonlinear Block-Oriented Systems starting from Linear Approximations: A Survey. CoRR abs/1607.01217 (2016) - [i2]Anna Marconato, Maarten Schoukens, Johan Schoukens:
Filter-based regularisation for impulse response modelling. CoRR abs/1610.07353 (2016) - [i1]Koen Tiels, Maarten Schoukens, Johan Schoukens:
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines. CoRR abs/1612.04568 (2016) - 2015
- [j7]Maarten Schoukens, Anna Marconato, Rik Pintelon, Gerd Vandersteen, Yves Rolain:
Parametric identification of parallel Wiener-Hammerstein systems. Autom. 51: 111-122 (2015) - [j6]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) - [j5]Koen Tiels, Maarten Schoukens, Johan Schoukens:
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines. Autom. 60: 201-209 (2015) - [c6]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
- [j4]Maarten Schoukens, Rik Pintelon, Yves Rolain:
Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation. Autom. 50(2): 628-634 (2014) - [c5]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
- [c4]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: 372-377 - [c3]Anna Marconato, Maarten Schoukens, Yves Rolain, Johan Schoukens:
Study of the effective number of parameters in nonlinear identification benchmarks. CDC 2013: 4308-4313 - [c2]Maarten Schoukens, Gerd Vandersteen, Yves Rolain:
An identification algorithm for parallel Wiener-Hammerstein systems. CDC 2013: 4907-4912 - 2012
- [j3]Maarten Schoukens, Yves Rolain:
Cross-term Elimination in Parallel Wiener Systems Using a Linear Input Transformation. IEEE Trans. Instrum. Meas. 61(3): 845-847 (2012) - [j2]Maarten Schoukens, Yves Rolain:
Parametric Identification of Parallel Wiener Systems. IEEE Trans. Instrum. Meas. 61(10): 2825-2832 (2012) - 2011
- [j1]Maarten Schoukens, Rik Pintelon, Yves Rolain:
Parametric Identification of Parallel Hammerstein Systems. IEEE Trans. Instrum. Meas. 60(12): 3931-3938 (2011) - [c1]Maarten Schoukens, Yves Rolain:
Parametric MIMO parallel Wiener identification. CDC/ECC 2011: 5100-5105
Coauthor Index
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