Koen Tiels
Koen Tiels
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Zitiert von
Zitiert von
Identification of block-oriented nonlinear systems starting from linear approximations: A survey
M Schoukens, K Tiels
Automatica 85, 272-292, 2017
Deep Learning and System Identification
L Ljung, C Andersson, K Tiels, TB Schön
21st IFAC World Congress, 8, 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
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
On the smoothness of nonlinear system identification
AH Ribeiro, K Tiels, J Umenberger, TB Schön, LA Aguirre
Automatica 121, 109158, 2020
Wiener system identification with generalized orthonormal basis functions
K Tiels, J Schoukens
Automatica 50 (12), 3147-3154, 2014
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
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
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
Initial estimates for Wiener–Hammerstein models using phase-coupled multisines
K Tiels, M Schoukens, J Schoukens
Automatica 60, 201-209, 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
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
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
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
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
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
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
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
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
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
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