109 citations · 955 across the 41 of their papers we have counts for
30 papers · 1 filter
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 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…
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…
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…
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…
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…
Structure Discrimination in Block-Oriented Models Using Linear Approximations: A Theoretic Framework
Johan Schoukens, Rik Pintelon, Yves Rolain +5
In this paper we show that it is possible to retrieve structural information about complex block-oriented nonlinear systems, starting from linear approximations of the nonlinear sy…
Linear System Identification in a Nonlinear Setting - Nonparametric analysis of the nonlinear distortions and their impact on the best linear approximation
Johan Schoukens, Mark Vaes, Rik Pintelon
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.…
Improved Initialization for Nonlinear State-Space Modeling
A. Marconato, J. Sjöberg, J. A. K. Suykens +1
This paper discusses a novel initialization algorithm for the estimation of nonlinear state-space models. Good initial values for the model parameters are obtained by identifying s…
Nonlinear state-space modelling of the kinematics of an oscillating circular cylinder in a fluid flow
Jan Decuyper, Tim De Troyer, Mark Runacres +2
The flow-induced vibration of bluff bodies is an important problem of many marine, civil, or mechanical engineers. In the design phase of such structures, it is vital to obtain goo…
Extending the Best Linear Approximation Framework to the Process Noise Case
Maarten Schoukens, Rik Pintelon, Tadeusz P. Dobrowiecki +1
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 fr…
Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example
Anna Marconato, Maarten Schoukens, Koen Tiels +3
In this paper, several advanced data-driven nonlinear identification techniques are compared on a specific problem: a simplified glucoregulatory system modeling example. This probl…
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…
D-Optimal Input Design for Nonlinear FIR-type Systems:A Dispersion-based Approach
Alexander De Cock, Michel Gevers, Johan Schoukens
Optimal input design is an important step of the identification process in order to reduce the model variance. In this work a D-optimal input design method for finite-impulse-respo…
Initial estimates for Wiener-Hammerstein models using phase-coupled multisines
Koen Tiels, Maarten Schoukens, Johan Schoukens
Block-oriented models are often used to model nonlinear systems. These models consist of linear dynamic (L) and nonlinear static (N) sub-blocks. This paper addresses the generation…