Rik Pintelon
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View article: Improved frequency response function estimation by Gaussian process regression with prior knowledge
Improved frequency response function estimation by Gaussian process regression with prior knowledge Open
Kernel-based modelling of dynamical systems offers important advantages such as imposing stability, causality and smoothness on the estimate of the model. Here, we improve the existing frequency domain kernel-based approach for estimating …
View article: An LTV Approach to Identifying Nonlinear Systems-with Application to an RRR-Robot
An LTV Approach to Identifying Nonlinear Systems-with Application to an RRR-Robot Open
Nonlinear systems are appearing in all engineering applications. Deriving models for these systems is important for instance for prediction and control. The goal of this paper is to estimate models of a class of nonlinear systems, from exp…
View article: Harmonic Analysis for the Separation of Perfusion and Respiration in Electrical Impedance Tomography
Harmonic Analysis for the Separation of Perfusion and Respiration in Electrical Impedance Tomography Open
Electrical Impedance Tomography (EIT) is mainly used to display information about the respiration of a patient. However, also cardiac-related signals are present, and, although they have small amplitude, they can be distinguished by their …
View article: Best Linear Approximation of Nonlinear Continuous-Time Systems Subject to Process Noise and Operating in Feedback
Best Linear Approximation of Nonlinear Continuous-Time Systems Subject to Process Noise and Operating in Feedback Open
In many engineering applications, the level of nonlinear distortions in frequency response function (FRF) measurements is quantified using specially designed periodic excitation signals called random phase multisines and periodic noise. Th…
View article: Nonparametric Identification of Linear Time-Varying Systems using Gaussian Process Regression
Nonparametric Identification of Linear Time-Varying Systems using Gaussian Process Regression Open
Linear time-varying systems are a class of systems, the dynamics of which evolve in time. This results in a time-varying frequency response function where each frequency has a time-varying gain. In classical identification techniques, basi…
View article: Experimental Validation of a Data-Driven Step Input Estimation Method for Dynamic Measurements
Experimental Validation of a Data-Driven Step Input Estimation Method for Dynamic Measurements Open
sponsorship: This work was supported in part by the European Research Council (ERC) through the European Union's Seventh Framework Program (FP7/2007-2013)/ERC under Grant Agreement 258581 (Structured Low-Rank Approximation: Theory, Algorit…
View article: On Local LTI Model Coherence for LPV Interpolation
On Local LTI Model Coherence for LPV Interpolation Open
In the local approach to linear parameter varying (LPV) system identification, it is widely acknowledged that locally estimated linear state-space models should be made coherent before being interpolated, but the accurate meaning of the te…
View article: Extending the Best Linear Approximation Framework to the Process Noise Case
Extending the Best Linear Approximation Framework to the Process Noise Case Open
The Best Linear Approximation (BLA) framework has already proven to be a valuable tool to analyze nonlinear systems and to start the nonlinear modeling process. The existing BLA framework is limited to systems with additive (colored) noise…
View article: Impact of the Missing Data Pattern, the Oversampling, the Noise Level, and the Excitation on Nonparametric Frequency Response Function Estimates
Impact of the Missing Data Pattern, the Oversampling, the Noise Level, and the Excitation on Nonparametric Frequency Response Function Estimates Open
Nonparametric frequency response function estimation (FRF) is a first important step towards successful parametric modelling of the dynamics. In some applications such as, for example, low-cost wireless sensor networks, sensors are subject…
View article: Continuous‐time linear time‐varying system identification with a frequency‐domain kernel‐based estimator
Continuous‐time linear time‐varying system identification with a frequency‐domain kernel‐based estimator Open
A novel estimator for the identification of continuous‐time linear time‐varying systems is presented in this paper. The estimator uses kernel‐based regression to identify the time‐varying coefficients of a linear ordinary differential equa…
View article: Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation
Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation Open
This article addresses the following problems: 1) First, a nonlinearity analysis is made looking for the presence of nonlinearities in an early phase of the identification process. The level and the nature of the nonlinearities should be r…