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Partial moments in system identification

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Partial moments in system identification/ by Régis Ouvrard, Thierry Poinot, Jean-Claude Trigeassou.
作者:	Ouvrard, Régis.
其他作者:	Trigeassou, Jean-Claude.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xii, 172 p. :ill. (chiefly col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Discrete Mathematics in Computer Science. -
電子資源:	https://doi.org/10.1007/978-3-031-58156-4
ISBN:	9783031581564

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 ;382..

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

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

670123
Discrete Mathematics in Computer Science.

LC Class. No.: QA402

Dewey Class. No.: 003.1

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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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