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Regularized System Identification

- Learning Dynamic Models from Data
Af: Gianluigi Pillonetto, Tianshi Chen, Lennart Ljung, Alessandro Chiuso, Giuseppe De Nicolao Engelsk Paperback

Regularized System Identification

- Learning Dynamic Models from Data
Af: Gianluigi Pillonetto, Tianshi Chen, Lennart Ljung, Alessandro Chiuso, Giuseppe De Nicolao Engelsk Paperback
Tjek vores konkurrenters priser
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.This is an open access book.
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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.This is an open access book.
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Produktdetaljer
Sprog: Engelsk
Sider: 377
ISBN-13: 9783030958626
Indbinding: Paperback
Udgave:
ISBN-10: 3030958620
Kategori: Kybernetik og systemteori
Udg. Dato: 14 maj 2022
Længde: 28mm
Bredde: 234mm
Højde: 155mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 14 maj 2022
Forfatter(e): Gianluigi Pillonetto, Tianshi Chen, Lennart Ljung, Alessandro Chiuso, Giuseppe De Nicolao
Forfatter(e) Gianluigi Pillonetto, Tianshi Chen, Lennart Ljung, Alessandro Chiuso, Giuseppe De Nicolao

Kategori Kybernetik og systemteori

ISBN-13 9783030958626

Sprog Engelsk

Indbinding Paperback

Sider 377

Udgave

Længde 28mm

Bredde 234mm

Højde 155mm

Udg. Dato 14 maj 2022

Oplagsdato 14 maj 2022

Forlag Springer Nature Switzerland AG

Kategori sammenhænge