| Identification of block-oriented nonlinear systems starting from linear approximations: A survey M Schoukens, K Tiels Automatica 85, 272-292, 2017 | 251 | 2017 |
| Deep Learning and System Identification L Ljung, C Andersson, K Tiels, TB Schön 21st IFAC World Congress, 8, 2020 | 160 | 2020 |
| Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness AH Ribeiro, K Tiels, LA Aguirre, T Schön International Conference on Artificial Intelligence and Statistics, 2370-2380, 2020 | 133 | 2020 |
| Deep convolutional networks in system identification C Andersson, AH Ribeiro, K Tiels, N Wahlström, TB Schön 2019 IEEE 58th Conference on Decision and Control (CDC), 3670-3676, 2019 | 72 | 2019 |
| On the smoothness of nonlinear system identification AH Ribeiro, K Tiels, J Umenberger, TB Schön, LA Aguirre Automatica 121, 109158, 2020 | 69 | 2020 |
| Wiener system identification with generalized orthonormal basis functions K Tiels, J Schoukens Automatica 50 (12), 3147-3154, 2014 | 67 | 2014 |
| Parameter reduction in nonlinear state-space identification of hysteresis AF Esfahani, P Dreesen, K Tiels, JP Noël, J Schoukens Mechanical Systems and Signal Processing 104, 884-895, 2018 | 37 | 2018 |
| Structure discrimination in block-oriented models using linear approximations: A theoretic framework J Schoukens, R Pintelon, Y Rolain, M Schoukens, K Tiels, L Vanbeylen, ... Automatica 53, 225-234, 2015 | 37 | 2015 |
| Nonlinear state-space modelling of the kinematics of an oscillating circular cylinder in a fluid flow J Decuyper, T De Troyer, MC Runacres, K Tiels, J Schoukens Mechanical Systems and Signal Processing 98, 209-230, 2018 | 30 | 2018 |
| Initial estimates for Wiener–Hammerstein models using phase-coupled multisines K Tiels, M Schoukens, J Schoukens Automatica 60, 201-209, 2015 | 25 | 2015 |
| Identification of parallel Wiener-Hammerstein systems with a decoupled static nonlinearity M Schoukens, K Tiels, M Ishteva, J Schoukens IFAC Proceedings Volumes 47 (3), 505-510, 2014 | 25 | 2014 |
| Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling J Decuyper, K Tiels, MC Runacres, J Schoukens Mechanical Systems and Signal Processing 146, 106966, 2021 | 23 | 2021 |
| From coupled to decoupled polynomial representations in parallel Wiener-Hammerstein models K Tiels, J Schoukens 52nd IEEE Conference on Decision and Control, 4937-4942, 2013 | 23 | 2013 |
| System identification in a real world J Schoukens, A Marconato, R Pintelon, Y Rolain, M Schoukens, K Tiels, ... 2014 IEEE 13th International Workshop on Advanced Motion Control (AMC), 1-9, 2014 | 22 | 2014 |
| Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach P Dreesen, M Schoukens, K Tiels, J Schoukens 2015 IEEE International Instrumentation and Measurement Technology …, 2015 | 21 | 2015 |
| Decoupling multivariate polynomials for nonlinear state-space models J Decuyper, P Dreesen, J Schoukens, MC Runacres, K Tiels IEEE Control Systems Letters 3 (3), 745-750, 2019 | 20 | 2019 |
| An Unstructured Flexible Nonlinear Model for the Cascaded Water-tanks Benchmark R Relan, K Tiels, A Marconato, J Schoukens IFAC-PapersOnLine 50 (1), 452-457, 2017 | 19 | 2017 |
| Sampled-data adaptive observer for state-affine systems with uncertain output equation T Ahmed-Ali, K Tiels, M Schoukens, F Giri Automatica 103, 96-105, 2019 | 18 | 2019 |
| Polynomial state-space model decoupling for the identification of hysteretic systems AF Esfahani, P Dreesen, K Tiels, JP Noël, J Schoukens IFAC-PapersOnLine 50 (1), 458-463, 2017 | 16 | 2017 |
| Hammerstein system identification through best linear approximation inversion and regularisation R Castro-Garcia, K Tiels, OM Agudelo, JAK Suykens International Journal of Control 91 (8), 1757-1773, 2018 | 14 | 2018 |
Maarten SchoukensAssociate Professor, Eindhoven University of Technology (TU/e)Verified email at tue.nl
