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Regularized System Identification - Learning Dynamic Models from Data (Paperback, 1st ed. 2022)
Loot Price: R1,485
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Regularized System Identification - Learning Dynamic Models from Data (Paperback, 1st ed. 2022)
Series: Communications and Control Engineering
Expected to ship within 10 - 15 working days
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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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