109 citations · 955 across the 41 of their papers we have counts for
45 papers
Decoupling multivariate functions using a nonparametric filtered tensor decomposition
Jan Decuyper, Koen Tiels, Siep Weiland +2
Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are expressions in the form of basis expansions or neural networks. While highly ef…
Applying Polynomial Decoupling Methods to the Polynomial NARX Model
Kiana Karami, David Westwick, Johan Schoukens
System identification uses measurements of a dynamic system's input and output to reconstruct a mathematical model for that system. These can be mechanical, electrical, physiologic…
A frequency domain approach for local module identification in dynamic networks
Karthik R. Ramaswamy, Péter Zoltán Csurcsia, Johan Schoukens +1
In classical approaches of dynamic network identification, in order to identify a system (module) embedded in a dynamic network, one has to formulate a Multi-input-Single-output (M…
PNLSS Toolbox 1.0
Jan Decuyper, Koen Tiels, Johan Schoukens
This is a demonstration of the PNLSS Toolbox 1.0. The toolbox is designed to identify polynomial nonlinear state-space models from data. Nonlinear state-space models can describe a…
Decoupling P-NARX models using filtered CPD
Jan Decuyper, David Westwick, Kiana Karami +1
Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often a polynomial basis expansion is used to describe the internal multi…
Decoupling multivariate functions using a non-parametric Filtered CPD approach
Jan Decuyper, Koen Tiels, Siep Weiland +1
Black-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the fun…
A nonlinear model of vortex-induced forces on an oscillating cylinder in a fluid flow
Jan Decuyper, Tim De Troyer, Koen Tiels +2
A nonlinear model relating the imposed motion of a circular cylinder, submerged in a fluid flow, to the transverse force coefficient is presented. The nonlinear fluid system, featu…
Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling
Jan Decuyper, Koen Tiels, Mark C. Runacres +1
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 parameter…
Nonlinear System Identification: A User-Oriented Roadmap
Johan Schoukens, Lennart Ljung
The goal of this article is twofold. Firstly, nonlinear system identification is introduced to a wide audience, guiding practicing engineers and newcomers in the field to a sound s…
Data-Driven Nonlinear Identification of Li-Ion Battery Based on a Frequency Domain Nonparametric Analysis
Rishi Relan, Yousef Firouz, Jean-Marc Timmermans +1
Lithium ion batteries are attracting significant and growing interest, because their high energy and high power density render them an excellent option for energy storage, particul…
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,…
A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets
Rishi Relan, Koen Tiels, Jean-Marc Timmermans +1
Battery short-term electrical impedance behavior varies between linear, linear time-varying, or nonlinear at different operating conditions. Data-based electrical impedance modelin…
Recursive Discrete-Time Models for Continuous-Time Systems Under Band-Limited Assumptions
Rishi Relan, Johan Schoukens
Discrete-time models are very convenient to simulate a nonlinear system on a computer. In order to build the discrete-time simulation models for the nonlinear feedback systems (whi…
Using the Best Linear Approximation With Varying Excitation Signals for Nonlinear System Characterization
Alireza Fakhrizadeh Esfahani, Johan Schoukens, Laurent Vanbeylen
Block oriented model structure detection is quite desirable since it helps to imagine the system with real physical elements. In this work we explore experimental methods to detect…
Measuring the Noise Cumulative Distribution Function Using Quantized Data
Paolo Carbone, Johan Schoukens, István Kollár +1
This paper considers the problem of estimating the cumulative distribution function and probability density function of a random variable using data quantized by uniform and non-un…
Improved Tactile Resonance Sensor for Robotic Assisted Surgery
David Oliva Uribe, Johan Schoukens, Ralf Stroop
This paper presents an improved tactile sensor using a piezoelectric bimorph able to differentiate soft materials with similar mechanical characteristics. The final aim is to devel…
Grey-box state-space identification of nonlinear mechanical vibrations
Jean-Philippe Noël, Johan Schoukens
The present paper deals with the identification of nonlinear mechanical vibrations. A grey-box, or semi-physical, nonlinear state-space representation is introduced, expressing the…
Accurate Sine-Wave Amplitude Measurements Using Nonlinearly Quantized Data
Paolo Carbone, Johan Schoukens, István Kollár +1
The estimation of the amplitude of a sine wave from the sequence of its quantized samples is a typical problem in instrumentation and measurement. A standard approach for its solut…
Regularized Nonparametric Volterra Kernel Estimation
Georgios Birpoutsoukis, Anna Marconato, John Lataire +1
In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modelled as Volterr…
Information and Statistical Efficiency When Quantizing Noisy DC Values
Antonio Moschitta, Johan Schoukens, Paolo Carbone
This paper considers estimation of a quantized constant in noise when using uniform and nonuniform quantizers. Estimators based on simple arithmetic averages, on sample statistical…
Dynamic Signal Measurements Based on Quantized Data
Paolo Carbone, Johan Schuokens, Antonio Moschitta
The estimation of the parameters of a dynamic signal, such as a sine wave, based on quantized data is customarily performed using the least-square estimator (LSE), such as the sine…
Efficient Multidimensional Regularization for Volterra Series Estimation
Georgios Birpoutsoukis, Péter Zoltán Csurcsia, Johan Schoukens
This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated wit…
Structure detection of Wiener-Hammerstein systems with process noise
Erliang Zhang, Maarten Schoukens, Johan Schoukens
Identification of nonlinear block-oriented models has been extensively studied. The presence of the process noise, more precisely its location in the block-oriented model influence…
Parametric System Identification Using Quantized Data
Antonio Moschitta, Johan Schoukens, Paolo Carbone
The estimation of signal parameters using quantized data is a recurrent problem in electrical engineering. As an example, this includes the estimation of a noisy constant value and…