Publicaties
A data-driven nonlinear state-space model of the unsteady lift force on a pitching wing Vrije Universiteit Brussel
Decoupling in Black Box Nonlinear System Identification: A Game Changer Vrije Universiteit Brussel
One-bit System Identification Vrije Universiteit Brussel
Nonlinear System Identification with Dominating Output Noise - A Case Study on the Silverbox Vrije Universiteit Brussel
Applying polynomial decoupling methods to the polynomial NARX model Vrije Universiteit Brussel
Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling Vrije Universiteit Brussel
Nonlinear state-space modelling is a very powerful black-box modelling approach. However powerful, the resulting models tend to be complex, described by a large number of parameters. In many cases interpretability is preferred over complexity, making too complex models unfit or undesired. In this work, the complexity of such models is reduced by retrieving a more structured, parsimonious model from the data, without exploiting physical ...
User-friendly nonlinear nonparametric estimation framework for vibro-acoustic industrial measurements with multiple inputs Vrije Universiteit Brussel
This paper introduces a user-friendly estimation framework for industrial measurements of vibro-acoustic systems with multiple inputs. Many mechanical structures are inherently nonlinear and there is no unique solution for modeling nonlinear systems. This is especially true when multiple-input, multiple-output (MIMO) systems are considered. This paper addresses the questions related to the user-friendly semi-automatic processing of MIMO ...
A1.5-bit DFT Analyzer Vrije Universiteit Brussel
Estimating a nonparametric data-driven model of the lift on a pitching wing Vrije Universiteit Brussel
In aerodynamics, as in many engineering applications, a parametrised mathematical model is used for design and control. Often, such models are directly estimated from experimental data. However, in some cases, it is better to first identify a so-called nonparametric model, before moving to a parametric model. Especially when nonlinear effects are present, a lot of information can be gained from the nonparametric model and the resulting ...