EAN: 9783030958626

COMMUNICATIONS AND CONTROL ENGINEERING REGULA...

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.

Aktuelle Preise zur EAN 9783030958626

Produktinformationen zur 9783030958626