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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Schoukens, Johan
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2021One-bit System Identificationcitations
- 2017NONLINEAR SYSTEM IDENTIFICATION: FINDING STRUCTURE IN NONLINEAR BLACK-BOX MODELS
- 2016Orbital component extraction by time-variant sinusoidal modeling
- 2013Three free data sets for development and benchmarking in nonlinear system identification
- 2007Practical aspects of continuous-time modelling from noisy observations
- 2004Electrochemical impedance spectroscopy in the presence of non-linear distortions and non-stationary behaviour Part I: theory and validation
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booksection
Practical aspects of continuous-time modelling from noisy observations
Abstract
System identification is an established field in the area of system analysis and control. It aims to determine particular models for dynamical systems based on observed inputs and outputs. Although dynamical systems in the physical world are naturally described in the continuous-time domain, most system identification schemes have been based on discrete-time models without concern for the merits of natural continuous-time model descriptions. The continuous-time nature of physical laws, the persistent popularity of predominantly continuous-time proportional-integral-derivative control and the more direct nature of continuous-time fault diagnosis methods make continuous-time modeling of ongoing importance.<br/>Identification of Continuous-time Models from Sampled Data brings together contributions from well-known experts who present an up-to-date view of this active area of research and describe recent methods and software tools developed in this field. They offer a fresh look at and new results in areas such as: <br/>*time and frequency domain optimal statistical approaches to identification; <br/>*parametric identification for linear, nonlinear and stochastic systems;<br/>*identification using instrumental variable, subspace and data compression methods; <br/>*closed-loop and robust identification; and <br/>*continuous-time modeling from non-uniformly sampled data and for systems with delay.<br/>The Continuous-Time System Identification (CONTSID) toolbox described in the book gives an overview of developments and practical examples in which MATLAB® can be brought to bear in the cause of direct time-domain identification of continuous-time systems.This survey of methods and results in continuous-time system identification will be a valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control. It also covers comprehensive material suitable for specialised graduate courses in these areas.