Regularized system identification : learning dynamic models from data / Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung.
Pillonetto, Gianluigi, author.; Chen, Tianshi, author.; Chiuso, Alessandro, author.; De Nicolao, Giuseppe, author.; Ljung, Lennart, author.
2022
QA402
Available Online

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Open access
Document Delivery Supplied
Open access

Details

Title
Regularized system identification : learning dynamic models from data / Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung.
Author
Pillonetto, Gianluigi, author.
ISBN
9783030958602 (electronic bk.)
3030958604 (electronic bk.)
9783030958596 (print)
DOI
https://doi.org/10.1007/978-3-030-95860-2
Published
Cham, Switzerland : Springer, 2022.
Language
English
Description
1 online resource (xxiv, 377 pages) : illustrations (some color).
Item Number
10.1007/978-3-030-95860-2 doi
Call Number
QA402
Dewey Decimal Classification
003/.1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Open access.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed May 19, 2022).
Added Author
Chen, Tianshi, author.
Chiuso, Alessandro, author.
De Nicolao, Giuseppe, author.
Ljung, Lennart, author.
Series
Communications and control engineering, 2197-7119
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export