Regularized System Identification Learning Dynamic Models from Data

Regularized System Identification Learning Dynamic Models from Data

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
Format: eBook
Language:English
Published: Cham Springer Nature 2022
Series:Communications and Control Engineering
Subjects:
Automatic control engineering
Bayesian inference
Cybernetics and systems theory
Machine learning
Probability and statistics
Statistical physics
Bayesian Interpretation of Regularization
Estimation Theory
Gaussian Processes
Kernel-based Regularization
Linear Dynamical Systems
Machine Learning
Nonlinear Dynamical Systems
Regularization Networks
Reproducing Kernel Hilbert Spaces
Support Vector Machines
System Identification
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Open Access: DOAB: description of the publication
Open Access: DOAB, download the publication

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