Regularized System Identification: Learning Dynamic Models from Data

Regularized System Identification: Learning Dynamic Models from Data

Machine learning
Automatic control engineering
Statistical physics
Bayesian inference
Probability & statistics
Cybernetics & systems theory

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.

Pillonetto, Gianluigi
Chen, Tianshi
Chiuso, Alessandro
De Nicolao, Giuseppe
Ljung, Lennart

https://directory.doabooks.org/handle/20.500.12854/84390

Springer Nature

2022

Wahyuni

http://creativecommons.org/licenses/by/4.0/

Pdf

English

Textbooks

DOI
10.1007/978-3-030-95860-2

ISBN
9783030958602