scispace - formally typeset
Search or ask a question
Author

Johan Schoukens

Bio: Johan Schoukens is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Nonlinear system & System identification. The author has an hindex of 25, co-authored 173 publications receiving 2215 citations. Previous affiliations of Johan Schoukens include Budapest University of Technology and Economics.


Papers
More filters
Journal ArticleDOI
TL;DR: This paper shows that it is possible to deal with nonperiodic signals without any approximation and under the same assumptions as in the time domain, by estimating simultaneously some initial conditions and the system model parameters.
Abstract: It is the common conviction that frequency domain system identification suffers from the drawback that it cannot handle arbitrary signals without introducing systematic errors. This paper shows that it is possible to deal with nonperiodic signals without any approximation and under the same assumptions as in the time domain, by estimating simultaneously some initial conditions and the system model parameters.

160 citations

01 Jan 2009
TL;DR: In this article, a Wiener-Hammerstein system is selected as a test object for nonlinear system identification in the presence of two unknown dynamic systems, and the focus of the benchmark is on the ability to identify the nonlinear behaviour, and not so much on the noise rejection properties.
Abstract: This paper describes a benchmark for nonlinear system identification A Wiener-Hammerstein system is selected as test object In such a structure there is no direct access to the static nonlinearity starting from the measured input/output, because it is sandwiched between two unknown dynamic systems The signal-to-noise ratio of the measurements is quite high, which puts the focus of the benchmark on the ability to identify the nonlinear behaviour, and not so much on the noise rejection properties The benchmark is not intended as a competition, but as a tool to compare the possibilities of different methods to deal with this specific nonlinear structure

113 citations

16 Sep 2002
TL;DR: This paper deals with the identification of the Hammerstein-Wiener model, which is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output.
Abstract: In nonlinear system identification, the system is often represented as a series of blocks linked together. Such block-oriented models are built with static nonlinear subsystems and linear dynamic systems. This paper deals with the identification of the Hammerstein-Wiener model, which is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output. The proposed identification scheme is iterative and will be demonstrated on measurements. It will be proven that on noiseless data and in absence of modeling errors, the optimization procedure converges to the true system locally.

79 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed an optimal multisine excitation for electrical bioimpedance measurements, which is obtained by the minimization of the Cramer-Rao lower bound by maximizing the accuracy obtained from the measurements.
Abstract: Electrical impedance spectroscopy (EIS) can be used to characterize biological materials in applications ranging from cell culture to body composition, including tissue and organ state. The emergence of cell therapy and tissue engineering opens up a new and promising field of application. While in most cases classical measurement techniques based on a frequency sweep can be used, EIS based on broadband excitations enables dynamic biological systems to be characterized when the measuring time and injected energy are a constraint. Myocardial regeneration, cell characterization in micro-fluidic systems and dynamic electrical impedance tomography are all examples of such applications. The weakness of such types of fast EIS measuring techniques resides in their intrinsic loss of accuracy. However, since most of the practical applications have no restriction over the excitation used, the input power spectrum can be appropriately designed to maximize the accuracy obtained from the measurements. This paper deals with the problem of designing the optimal multisine excitation for electrical bioimpedance measurements. The optimal multisine is obtained by the minimization of the Cramer–Rao lower bound, or what is the same, by maximizing the accuracy obtained from the measurements. Furthermore, because no analytical solution exists for global optimization involving time and frequency domains jointly, this paper presents the multisine optimization approach partially in both domains and then combines the results. As regards the frequency domain approach, a novel contribution is made for the multisine amplitude power spectrum. In the time domain, multisine is optimized by reducing its crest factor. Moreover, the impact on the information and accuracy of the impedance spectrum obtained from using different multisine amplitude power spectra is discussed, as well as the number of frequencies and frequency distributions. The theory is supported by a set of validation measurements when exciting with the optimal and flat multisine signals and compared to a single frequency ac impedance analyzer when characterizing an RC circuit. In vivo healthy myocardium tissue electrical impedance measurements show that broadband EIS based on multisine excitations enable the characterization of dynamic biological systems.

70 citations

Journal ArticleDOI
TL;DR: In this paper, the identification of the Hammerstein-Wiener model is studied, which is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output.

69 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

5,689 citations

Book
01 Jan 1991
TL;DR: In this paper, the Third Edition of the Third edition of Linear Systems: Local Theory and Nonlinear Systems: Global Theory (LTLT) is presented, along with an extended version of the second edition.
Abstract: Series Preface * Preface to the Third Edition * 1 Linear Systems * 2 Nonlinear Systems: Local Theory * 3 Nonlinear Systems: Global Theory * 4 Nonlinear Systems: Bifurcation Theory * References * Index

1,977 citations

01 Nov 1981
TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

1,829 citations

Book ChapterDOI
11 Dec 2012

1,704 citations

Journal ArticleDOI
S. Biyiksiz1
01 Mar 1985
TL;DR: This book by Elliott and Rao is a valuable contribution to the general areas of signal processing and communications and can be used for a graduate level course in perhaps two ways.
Abstract: There has been a great deal of material in the area of discrete-time transforms that has been published in recent years. This book does an excellent job of presenting important aspects of such material in a clear manner. The book has 11 chapters and a very useful appendix. Seven of these chapters are essentially devoted to the Fourier series/transform, discrete Fourier transform, fast Fourier transform (FFT), and applications of the FFT in the area of spectral estimation. Chapters 8 through 10 deal with many other discrete-time transforms and algorithms to compute them. Of these transforms, the KarhunenLoeve, the discrete cosine, and the Walsh-Hadamard transform are perhaps the most well-known. A lucid discussion of number theoretic transforms i5 presented in Chapter 11. This reviewer feels that the authors have done a fine job of compiling the pertinent material and presenting it in a concise and clear manner. There are a number of problems at the end of each chapter, an appreciable number of which are challenging. The authors have included a comprehensive set of references at the end of the book. In brief, this book is a valuable contribution to the general areas of signal processing and communications. It can be used for a graduate level course in perhaps two ways. One would be to cover the first seven chapters in great detail. The other would be to cover the whole book by focussing on different topics in a selective manner. This book by Elliott and Rao is extremely useful to researchers/engineers who are working in the areas of signal processing and communications. It i s also an excellent reference book, and hence a valuable addition to one’s library

843 citations