58 citations · 116 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…