Philippe Dreesen

Proceedings of the Large-Scale Scientific Computing - 13th International Conference, 2021

Data-Driven Simulation for NARX Systems.

[BibT_eX]

[DOI]

Proceedings of the 29th European Signal Processing Conference, 2021

2020

Decoupling multivariate polynomials: Interconnections between tensorizations.

[BibT_eX]

[DOI]

Konstantin Usevich

J. Comput. Appl. Math., 2020

2019

Decoupling Multivariate Polynomials for Nonlinear State-Space Models.

[BibT_eX]

[DOI]

IEEE Control. Syst. Lett., 2019

Data-driven Simulation Using the Nuclear Norm Heuristic.

[BibT_eX]

[DOI]

Ivan Markovsky

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Approximate decoupling of multivariate polynomials using weighted tensor decomposition.

[BibT_eX]

[DOI]

Numer. Linear Algebra Appl., 2018

Multidimensional realisation theory and polynomial system solving.

[BibT_eX]

[DOI]

Int. J. Control, 2018

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record.

[BibT_eX]

[DOI]

CoRR, 2018

Multidimensional Realization Theory and Polynomial System Solving.

[BibT_eX]

[DOI]

CoRR, 2018

Decoupling Multivariate Functions Using Second-Order Information and Tensors.

[BibT_eX]

[DOI]

Jeroen De Geeter

Alireza Fakhrizadeh Esfahani

Proceedings of the Latent Variable Analysis and Signal Separation, 2018

2017

Parameter reduction in nonlinear state-space identification of hysteresis.

[BibT_eX]

[DOI]

CoRR, 2017

An Initialization Method for Nonlinear Model Reduction Using the CP Decomposition.

[BibT_eX]

[DOI]

Proceedings of the Latent Variable Analysis and Signal Separation, 2017

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

[BibT_eX]

[DOI]

Proceedings of the Latent Variable Analysis and Signal Separation, 2017

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

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017

2016

Weighted tensor decomposition for approximate decoupling of multivariate polynomials.

[BibT_eX]

[DOI]

CoRR, 2016

2015

Decoupling Multivariate Polynomials Using First-Order Information and Tensor Decompositions.

[BibT_eX]

[DOI]

Johan Schoukens

SIAM J. Matrix Anal. Appl., 2015

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

[BibT_eX]

[DOI]

Proceedings of the Latent Variable Analysis and Signal Separation, 2015

Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2015

2014

The Canonical Decomposition of Cnd and Numerical Gröbner and Border Bases.

[BibT_eX]

[DOI]

SIAM J. Matrix Anal. Appl., 2014

A fast recursive orthogonalization scheme for the Macaulay matrix.

[BibT_eX]

[DOI]

J. Comput. Appl. Math., 2014

Decoupling Multivariate Polynomials Using First-Order Information.

[BibT_eX]

[DOI]

Johan Schoukens

CoRR, 2014

2013

The Geometry of Multivariate Polynomial Division and Elimination.

[BibT_eX]

[DOI]

SIAM J. Matrix Anal. Appl., 2013

2012

Joint Regression and Linear Combination of Time Series for Optimal Prediction.

[BibT_eX]

[DOI]

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

Weighted/Structured Total Least Squares problems and polynomial system solving.

[BibT_eX]

[DOI]

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

maximum likelihood estimation and polynomial system solving.

[BibT_eX]

[DOI]