58 citations · 116 across the 7 of their papers we have counts for
9 papers
Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems
Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decomposi…
Decoupling multivariate functions using second-order information and tensors
Philippe Dreesen, Jeroen De Geeter, Mariya Ishteva
The power of multivariate functions is their ability to model a wide variety of phenomena, but have the disadvantages that they lack an intuitive or interpretable representation, a…
Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record
Rishi Relan, Koen Tiels, Anna Marconato +2
Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper,…
Multidimensional Realization Theory and Polynomial System Solving
Philippe Dreesen, Kim Batselier, Bart De Moor
Multidimensional systems are becoming increasingly important as they provide a promising tool for estimation, simulation and control, while going beyond the traditional setting of…
Parameter reduction in nonlinear state-space identification of hysteresis
Alireza Fakhrizadeh Esfahani, Philippe Dreesen, Koen Tiels +2
Hysteresis is a highly nonlinear phenomenon, showing up in a wide variety of science and engineering problems. The identification of hysteretic systems from input-output data is a…
Decoupling multivariate polynomials: interconnections between tensorizations
Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
Decoupling multivariate polynomials is useful for obtaining an insight into the workings of a nonlinear mapping, performing parameter reduction, or approximating nonlinear function…
Modeling Parallel Wiener-Hammerstein Systems Using Tensor Decomposition of Volterra Kernels
Philippe Dreesen, David Westwick, Johan Schoukens +1
Providing flexibility and user-interpretability in nonlinear system identification can be achieved by means of block-oriented methods. One of such block-oriented system structures…
Weighted tensor decomposition for approximate decoupling of multivariate polynomials
Gabriel Hollander, Philippe Dreesen, Mariya Ishteva +1
Multivariate polynomials arise in many different disciplines. Representing such a polynomial as a vector of univariate polynomials can offer useful insight, as well as more intuiti…
Decoupling Multivariate Polynomials Using First-Order Information
Philippe Dreesen, Mariya Ishteva, Johan Schoukens
We present a method to decompose a set of multivariate real polynomials into linear combinations of univariate polynomials in linear forms of the input variables. The method procee…