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Identification of Continuous-time Models from Sampled Data

Hugues Garnier

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ISBN 10: 1848001606 / ISBN 13: 9781848001602
Editore: Springer-Verlag Gmbh Mrz 2008, 2008
Nuovi Condizione: Neu
Da Rhein-Team Lörrach Ivano Narducci e.K. (Lörrach, Germania)

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Neuware - Identification of Continuous-time Models from Sampled Data presents an up-to-date view of this active area of research, describing recent methods and software tools and offering new results in areas such as: time and frequency domain optimal statistical approaches to identification; parametric identification for linear, nonlinear and stochastic systems; identification using instrumental variable, subspace and data compression methods; closed-loop and robust identification; and continuous-time modeling from non-uniformly sampled data and for systems with delay. The CONTSID toolbox discussed in the final chapter gives an overview of developments and practical examples in which MATLAB® can be used for direct time-domain identification of continuous-time systems. A valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control this book also covers material suitable for specialised graduate courses in these areas. 413 pp. Englisch. Codice inventario libreria 9781848001602

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Titolo: Identification of Continuous-time Models ...

Casa editrice: Springer-Verlag Gmbh Mrz 2008

Data di pubblicazione: 2008

Legatura: Buch

Condizione libro:Neu

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Riassunto:

This is the first book dedicated to direct continuous-time model identification for 15 years. It cuts down on time spent hunting through journals by providing an overview of much recent research in an increasingly busy field. The CONTSID toolbox discussed in the final chapter gives an overview of developments and practical examples in which MATLAB® can be used for direct time-domain identification of continuous-time systems. This is a valuable reference for a broad audience.

From the Back Cover:

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.

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:

· time and frequency domain optimal statistical approaches to identification;

· parametric identification for linear, nonlinear and stochastic systems;

· identification using instrumental variable, subspace and data compression methods;

· closed-loop and robust identification; and

· continuous-time modeling from non-uniformly sampled data and for systems with delay.

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.

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