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dc.creator Pillonetto, Gianluigi
dc.creator Chen, Tianshi
dc.creator Chiuso, Alessandro
dc.creator De Nicolao, Giuseppe
dc.creator Ljung, Lennart
dc.date 2022-06-21T04:03:27Z
dc.date 2022-06-21T04:03:27Z
dc.date 2022-06-20T19:31:13Z
dc.date 2022
dc.date.accessioned 2023-02-18T19:26:53Z
dc.date.available 2023-02-18T19:26:53Z
dc.identifier ONIX_20220620_9783030958602_20
dc.identifier https://library.oapen.org/handle/20.500.12657/56998
dc.identifier https://directory.doabooks.org/handle/20.500.12854/84390
dc.identifier https://library.oapen.org/bitstream/20.500.12657/56998/1/978-3-030-95860-2.pdf
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/249310
dc.description 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.
dc.format image/jpeg
dc.language eng
dc.publisher Springer Nature
dc.publisher Springer
dc.relation Communications and Control Engineering
dc.rights open access
dc.subject System Identification
dc.subject Machine Learning
dc.subject Linear Dynamical Systems
dc.subject Nonlinear Dynamical Systems
dc.subject Kernel-based Regularization
dc.subject Bayesian Interpretation of Regularization
dc.subject Gaussian Processes
dc.subject Reproducing Kernel Hilbert Spaces
dc.subject Estimation Theory
dc.subject Support Vector Machines
dc.subject Regularization Networks
dc.subject bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
dc.subject bic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJF Electronics engineering::TJFM Automatic control engineering
dc.subject bic Book Industry Communication::P Mathematics & science::PH Physics::PHS Statistical physics
dc.subject bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics::PBTB Bayesian inference
dc.subject bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics
dc.subject bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general::GPF Information theory::GPFC Cybernetics & systems theory
dc.title Regularized System Identification
dc.resourceType book
dc.alternateIdentifier 9783030958602
dc.alternateIdentifier 10.1007/978-3-030-95860-2
dc.licenseCondition open access
dc.licenseCondition n/a
dc.identifierdoi 10.1007/978-3-030-95860-2
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dc.relationisbn 9783030958602
dc.pages 377
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dc.placepublication Cham
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