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Regularized system identificationlearning dynamic models from data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Regularized system identificationby Gianluigi Pillonetto ... [et al.].
Reminder of title:	learning dynamic models from data /
other author:	Pillonetto, Gianluigi.
Published:	Cham :Springer International Publishing :2022.
Description:	xxiv, 377 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	System identification.
Online resource:	https://doi.org/10.1007/978-3-030-95860-2
ISBN:	9783030958602$q(electronic bk.)

Regularized system identificationlearning dynamic models from data /
Regularized system identificationlearning dynamic models from data /[electronic resource] :by Gianluigi Pillonetto ... [et al.]. - Cham :Springer International Publishing :2022. - xxiv, 377 p. :ill. (some col.), digital ;24 cm. - Communications and control engineering,2197-7119. - Communications and control engineering..

Chapter 1. Bias -- Chapter 2. Classical System Identification -- Chapter 3. Regularization of Linear Regression Models -- Chapter 4. Bayesian Interpretation of Regularization -- Chapter 5. Regularization for Linear System Identification -- Chapter 6. Regularization in Reproducing Kernel Hilbert Spaces -- Chapter 7. Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification -- Chapter 8. Regularization for Nonlinear System Identification -- Chapter 9. Numerical Experiments and Real-World Cases.

Open access.

This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors' reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. In many ways, this book is a complement and continuation of the much-used text book L. Ljung, System Identification, 978-0-13-656695-3. This is an open access book.

ISBN: 9783030958602$q(electronic bk.)

Standard No.: 10.1007/978-3-030-95860-2doiSubjects--Topical Terms:

182015
System identification.

LC Class. No.: QA402 / .P55 2022

Dewey Class. No.: 003.1

Regularized system identificationlearning dynamic models from data /
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245 0 0 $a Regularized system identification $h [electronic resource] : $b learning dynamic models from data / $c by Gianluigi Pillonetto ... [et al.].
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490 1 $a Communications and control engineering, $x 2197-7119
505 0 $a Chapter 1. Bias -- Chapter 2. Classical System Identification -- Chapter 3. Regularization of Linear Regression Models -- Chapter 4. Bayesian Interpretation of Regularization -- Chapter 5. Regularization for Linear System Identification -- Chapter 6. Regularization in Reproducing Kernel Hilbert Spaces -- Chapter 7. Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification -- Chapter 8. Regularization for Nonlinear System Identification -- Chapter 9. Numerical Experiments and Real-World Cases.
506 $a Open access.
520 $a This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors' reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. In many ways, this book is a complement and continuation of the much-used text book L. Ljung, System Identification, 978-0-13-656695-3. This is an open access book.
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