Regularized System Identification Learning Dynamic Models from Data

• 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

Summary

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.

Physical Description

1 electronic resource (377 p.)

ISBN

978-3-030-95860-2
9783030958602

Access

Open Access