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Partial moments in system identification

Record Type:	Electronic resources : Monograph/item
Title/Author:	Partial moments in system identificationby Régis Ouvrard, Thierry Poinot, Jean-Claude Trigeassou.
Author:	Ouvrard, Régis.
other author:	Poinot, Thierry.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xii, 172 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	System identification.
Online resource:	https://doi.org/10.1007/978-3-031-58156-4
ISBN:	9783031581564$q(electronic bk.)

Partial moments in system identification
Ouvrard, Régis.

Partial moments in system identification[electronic resource] /by Régis Ouvrard, Thierry Poinot, Jean-Claude Trigeassou. - Cham :Springer Nature Switzerland :2024. - xii, 172 p. :ill. (chiefly col.), digital ;24 cm. - Lecture notes in control and information sciences,v. 4941610-7411 ;. - Lecture notes in control and information sciences ;315..

Chapter 1. An introduction about moments in identification -- Chapter 2. An introductory example -- Chapter 3. Partial moments in continuous-time -- Chapter 4. Partial moments in discrete-time -- Chapter 5. Algebraic identification, a partial moment approach -- Chapter 6. Continuous-time subspace based method -- Chapter 7. Continuous-time linear parameter varying model -- Chapter 8. Multidimensional partial moments -- Chapter 9. Perspectives -- Referrences.

This book provides a complete round-up of developments concerned with the application of partial moments in system identification and data-driven modelling; it captures the essence of work carried out at the Laboratoire d'Informatique et d'Automatique pour les Systèmes for more than 40 years. The book begins with introductory material, describing both the mathematical tools associated with partial moments and reinitialized partial moments and an example demonstrating their use. The authors then proceed to show how these tools can be used for the identification of continuous-time linear models, discrete-time linear models, continuous-time linear state-space models, linear parameter-varying models and multidimensional models based on partial differential equations. The properties and performances of each of these approaches are presented. The analogy with algebraic approaches is proved, thus opening perspectives for extension to other fields. The text removes some long-standing limitations on the implementation of partial-moment-based tools in system identification. This book is of interest to researchers and postgraduates studying system identification, control theory, applied mathematics and computer science. It is also useful for engineers working on industrial applications of parametric estimation of mathematical models.

ISBN: 9783031581564$q(electronic bk.)

Standard No.: 10.1007/978-3-031-58156-4doiSubjects--Topical Terms:

182015
System identification.

LC Class. No.: QA402

Dewey Class. No.: 003.1

Partial moments in system identification
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520 $a This book provides a complete round-up of developments concerned with the application of partial moments in system identification and data-driven modelling; it captures the essence of work carried out at the Laboratoire d'Informatique et d'Automatique pour les Systèmes for more than 40 years. The book begins with introductory material, describing both the mathematical tools associated with partial moments and reinitialized partial moments and an example demonstrating their use. The authors then proceed to show how these tools can be used for the identification of continuous-time linear models, discrete-time linear models, continuous-time linear state-space models, linear parameter-varying models and multidimensional models based on partial differential equations. The properties and performances of each of these approaches are presented. The analogy with algebraic approaches is proved, thus opening perspectives for extension to other fields. The text removes some long-standing limitations on the implementation of partial-moment-based tools in system identification. This book is of interest to researchers and postgraduates studying system identification, control theory, applied mathematics and computer science. It is also useful for engineers working on industrial applications of parametric estimation of mathematical models.
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