Philippe Dreesen

Johan Schoukens, Koen Tiels, Maarten Schoukens, 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

A fast recursive orthogonalization scheme for the Macaulay matrix

Bart De Moor, Kim Batselier, Philippe Dreesen, 2014, J. Comput. Appl. Math..

The Geometry of Multivariate Polynomial Division and Elimination

Bart De Moor, Kim Batselier, Philippe Dreesen, 2013, SIAM J. Matrix Anal. Appl..

Parameter reduction in nonlinear state-space identification of hysteresis pdf

Johan Schoukens, Jean-Philippe Noël, Koen Tiels, 2017, ArXiv.

Decoupling Multivariate Polynomials Using First-Order Information pdf

Johan Schoukens, Philippe Dreesen, Mariya Ishteva, 2014, ArXiv.

An Initialization Method for Nonlinear Model Reduction Using the CP Decomposition

Johan Schoukens, Philippe Dreesen, Mariya Ishteva, 2017, LVA/ICA.

Joint Regression and Linear Combination of Time Series for Optimal Prediction

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2012, ESANN.

Modeling Parallel Wiener-Hammerstein Systems Using Tensor Decomposition of Volterra Kernels

Johan Schoukens, David T. Westwick, Philippe Dreesen, 2016, LVA/ICA.

Decoupling Multivariate Functions Using Second-Order Information and Tensors

Philippe Dreesen, Mariya Ishteva, Jeroen De Geeter, 2018, LVA/ICA.

Block-Decoupling Multivariate Polynomials Using the Tensor Block-Term Decomposition

Lieven De Lathauwer, Johan Schoukens, Philippe Dreesen, 2015, LVA/ICA.

Decoupling multivariate polynomials: Interconnections between tensorizations

Konstantin Usevich, Philippe Dreesen, Mariya Ishteva, 2020, J. Comput. Appl. Math..

Data-driven Simulation Using the Nuclear Norm Heuristic

Philippe Dreesen, Ivan Markovsky, I. Markovsky, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Decoupling Multivariate Polynomials for Nonlinear State-Space Models

Johan Schoukens, Koen Tiels, Philippe Dreesen, 2019, IEEE Control Systems Letters.

Nonlinear system identification: Finding structure in nonlinear black-box models

Johan Schoukens, Koen Tiels, Philippe Dreesen, 2017, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

Parameter Estimation of Parallel Wiener-Hammerstein Systems by Decoupling their Volterra Representations

Philippe Dreesen, Mariya Ishteva, 2021, IFAC-PapersOnLine.

Data-Driven Simulation for NARX Systems*

Ivan Markovsky, Antonio Fazzi, Philippe Dreesen, 2021, 2021 29th European Signal Processing Conference (EUSIPCO).

Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems

Konstantin Usevich, Philippe Dreesen, Mariya Ishteva, 2021, IFAC-PapersOnLine.

Prediction Error Method Identification is an Eigenvalue Problem

Bart De Moor, Kim Batselier, Philippe Dreesen, 2012 .

Back to the Roots: Polynomial System Solving, Linear Algebra, Systems Theory

Bart De Moor, Kim Batselier, Philippe Dreesen, 2012 .

Back to the Roots: Polynomial System Solving Using Linear Algebra (Back to the Roots: Stelsels veeltermvergelijkingen oplossen met lineaire algebra)

Philippe Dreesen, 2013 .