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
Main Authors:	Pillonetto, Gianluigi (Author), Chen, Tianshi (Author), Chiuso, Alessandro (Author), De Nicolao, Giuseppe (Author), Ljung, Lennart (Author)
Corporate Author:	SpringerLink (Online service)
Format:	Electronic eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2022.
Edition:	1st ed. 2022.
Series:	Communications and Control Engineering,
Subjects:	Machine learning. Control engineering. System theory. Statistics . Control theory. Machine Learning. Control and Systems Theory. Complex Systems. Bayesian Inference. Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Systems Theory, Control.
Online Access:	Link to Metadata
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Table of Contents:

Chapter 1. Bias
Chapter 2. Classical System Identification
Chapter 3. Regularization of Linear Regression Models
Chapter 4. Bayesian Interpretation of Regularization
Chapter 5. Regularization for Linear System Identification
Chapter 6. Regularization in Reproducing Kernel Hilbert Spaces
Chapter 7. Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification
Chapter 8. Regularization for Nonlinear System Identification
Chapter 9. Numerical Experiments and Real-World Cases.

Regularized System Identification Learning Dynamic Models from Data /

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