Regularized System Identification - Learning Dynamic Models from Data (Paperback, 1st ed. 2022)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Communications and Control Engineering
Release date:	May 2022
First published:	2022
Authors:	Gianluigi Pillonetto • Tianshi Chen • Alessandro Chiuso • Giuseppe De Nicolao • Lennart Ljung
Dimensions:	235 x 155 x 28mm (L x W x T)
Format:	Paperback
Pages:	377
Edition:	1st ed. 2022
ISBN-13:	978-3-03-095862-6
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Science & Mathematics > Physics > Thermodynamics & statistical physics > Statistical physics Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General Promotions
LSN:	3-03-095862-0
Barcode:	9783030958626

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