Articoli correlati a Regularized System Identification: Learning Dynamic...
- EditoreSpringer-Nature New York Inc
- Data di pubblicazione2022
- ISBN 10 3030958590
- ISBN 13 9783030958596
- RilegaturaCopertina rigida
- Numero edizione1
- Numero di pagine404
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Regularized System Identification: Learning Dynamic Models from Data (Communications and Control Engineering)
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Descrizione libro Condizione: New. Book is in NEW condition. 1.86. Codice articolo 3030958590-2-1
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Regularized System Identification: Learning Dynamic Models from Data (Communications and Control Engineering)
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Descrizione libro Condizione: New. New! This book is in the same immaculate condition as when it was published 1.86. Codice articolo 353-3030958590-new
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Regularized System Identification: Learning Dynamic Models from Data (Communications and Control Engineering) by Pillonetto, Gianluigi, Chen, Tianshi, Chiuso, Alessandro, De Nicolao, Giuseppe, Ljung, Lennart [Hardcover ]
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Descrizione libro Hardcover. Condizione: new. Codice articolo 9783030958596
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Regularized System Identification
Editore:
Springer International Publishing Mai 2022
(2022)
ISBN 10: 3030958590
ISBN 13: 9783030958596
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Descrizione libro Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 soRegularized System Identificationwill 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. 404 pp. Englisch. Codice articolo 9783030958596
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Regularized System Identification : Learning Dynamic Models from Data
Editore:
Springer International Publishing
(2022)
ISBN 10: 3030958590
ISBN 13: 9783030958596
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Descrizione libro Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 soRegularized System Identificationwill 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. Codice articolo 9783030958596
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Regularized System Identification
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Springer International Publishing
(2022)
ISBN 10: 3030958590
ISBN 13: 9783030958596
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Descrizione libro Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Powerful tools lead to new principles and algorithms for various linear and nonlinear system identification techniquesCareful mathematics provide a rigorous basis for cross-fertilization between system identification and machine learningDev. Codice articolo 543920549
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