Regularized System Identification [[electronic resource] ] : Learning Dynamic Models from Data

• Autore: Pillonetto Gianluigi
• Pubblicazione: Cham, : Springer International Publishing AG, 2022
• Descrizione fisica: 1 online resource (394 p.)
• Soggetto topico: Machine learning; Automatic control engineering; Statistical physics; Bayesian inference; Probability & statistics; Cybernetics & systems theory
• Soggetto non controllato: 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
• Altri autori: ChenTianshi ChiusoAlessandro De NicolaoGiuseppe LjungLennart
• Note generali: Description based upon print version of record.
• Sommario/riassunto: 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.
• Titolo autorizzato: Regularized System Identification
• ISBN: 3-030-95860-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910568256103321
• Lo trovi qui: Univ. Federico II
• Serie: Communications and Control Engineering