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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Detalles Bibliográficos
Autor principal: Pillonetto, Gianluigi (-)
Otros Autores: Chen, Tianshi, Chiuso, Alessandro, De Nicolao, Giuseppe, Ljung, Lennart
Formato: Libro electrónico
Idioma:Inglés
Publicado: Cham : Springer International Publishing AG 2022.
Colección:Communications and Control Engineering
Materias:
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
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009666926406719

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