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Regularized system identification :learning dynamic models from data /

レコード種別:	言語・文字資料 (印刷物) : 単行資料
タイトル / 著者:	Regularized system identification :/ Gianluigi Pillonetto ... [et al.]
その他のタイトル:	learning dynamic models from data /
その他の著者:	Pillonetto, Gianluigi.
出版された:	Cham, Switzerland :Springer,c2022.
記述:	xxiv, 377 p. :ill. (some col.) ;24 cm.
主題:	System identification. -
国際標準図書番号 (ISBN):	9783030958626

Regularized system identification :learning dynamic models from data /
Regularized system identification :learning dynamic models from data / Gianluigi Pillonetto ... [et al.] - Cham, Switzerland :Springer,c2022. - xxiv, 377 p. :ill. (some col.) ;24 cm. - Communications and control engineering,2197-7119. - Communications and control engineering,..

Includes bibliographical references and index.

"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."-- adapted from jacket.

ISBN: 9783030958626NT1259Subjects--Topical Terms:

148878
System identification.

Dewey Class. No.: 003/.1

Regularized system identification :learning dynamic models from data /
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020 $z 9783030958602 $q (ebk.)
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245 1 0 $a Regularized system identification : $b learning dynamic models from data / $c Gianluigi Pillonetto ... [et al.]
260 $a Cham, Switzerland : $b Springer, $c c2022.
300 $a xxiv, 377 p. : $b ill. (some col.) ; $c 24 cm.
490 1 $a Communications and control engineering, $x 2197-7119
504 $a Includes bibliographical references and index.
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."-- adapted from jacket.
650 0 $a System identification. $3 148878
700 1 $a Pillonetto, Gianluigi. $3 1000148723
830 0 $a Communications and control engineering,. $3 1000148724