| 1 |
Extending the Best Linear Approximation Framework to the Process Noise Case
Schoukens M, Pintelon R, Dobrowiecki TP, Schoukens J
IEEE Transactions on Automatic Control, 65(4), 1514, 2020 |
| 2 |
Gaussian process regression for the estimation of generalized frequency response functions
Stoddard JG, Birpoutsoukis G, Schoukens J, Welsh JS
Automatica, 106, 161, 2019 |
| 3 |
Grey-box state-space identification of nonlinear mechanical vibrations
Noel JP, Schoukens J
International Journal of Control, 91(5), 1118, 2018 |
| 4 |
Regularized nonparametric Volterra kernel estimation
Birpoutsoukis G, Marconato A, Lataire J, Schoukens J
Automatica, 82, 324, 2017 |
| 5 |
The transient impulse response modeling method for non-parametric system identification
Hagg P, Schoukens J, Gevers M, Hjalmarsson H
Automatica, 68, 314, 2016 |
| 6 |
Information matrix and D-optimal design with Gaussian inputs for Wiener model identification
Mahata K, Schoukens J, De Cock A
Automatica, 69, 65, 2016 |
| 7 |
D-optimal input design for nonlinear FIR-type systems: A dispersion-based approach
De Cock A, Gevers M, Schoukens J
Automatica, 73, 88, 2016 |
| 8 |
Perturbed datasets methods for hypothesis testing and structure of corresponding confidence sets
Kolumban S, Vajk I, Schoukens J
Automatica, 51, 326, 2015 |
| 9 |
Structure discrimination in block-oriented models using linear approximations: A theoretic framework
Schoukens J, Pintelon R, Rolain Y, Schoukens M, Tiels K, Vanbeylen L, Van Mulders A, Vandersteen G
Automatica, 53, 225, 2015 |
| 10 |
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines
Tiels K, Schoukens M, Schoukens J
Automatica, 60, 201, 2015 |
