Regularized System Identification Learning Dynamic Models from Data

Regularized System Identification Learning Dynamic Models from Data

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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 learn...

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Bibliographic Details
Main Author: Pillonetto, Gianluigi (auth)
Other Authors: Chen, Tianshi (auth), Chiuso, Alessandro (auth), De Nicolao, Giuseppe (auth), Ljung, Lennart (auth)
Format: Electronic Book Chapter
Language:English
Published: Cham Springer Nature 2022
Series:Communications and Control Engineering
Subjects:
Machine learning
Automatic control engineering
Statistical physics
Bayesian inference
Probability & statistics
Cybernetics & systems theory
System Identification
Machine Learning
Linear Dynamical Systems
Nonlinear Dynamical Systems
Kernel-based Regularization
Bayesian Interpretation of Regularization
Gaussian Processes
Reproducing Kernel Hilbert Spaces
Estimation Theory
Support Vector Machines
Regularization Networks
Online Access:DOAB: download the publication
DOAB: description of the publication
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