Powerful tools lead to new principles and algorithms for various linear and nonlinear system identification techniques Careful mathematics provide a rigorous basis for cross-fertilization between system identification and machine learning Develops system identification principles in both deterministic and stochastic (Bayesian) settings This book is open access, which means that you have free and unlimited access.

Regularized System Identification

Gianluigi Pillonetto;Alessandro Chiuso;
2022

Abstract

Powerful tools lead to new principles and algorithms for various linear and nonlinear system identification techniques Careful mathematics provide a rigorous basis for cross-fertilization between system identification and machine learning Develops system identification principles in both deterministic and stochastic (Bayesian) settings This book is open access, which means that you have free and unlimited access.
2022
978-3-030-95859-6
978-3-030-95860-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3454985
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