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oapen-20.500.12657-569982022-06-21T03:05:36Z Regularized System Identification Pillonetto, Gianluigi Chen, Tianshi Chiuso, Alessandro De Nicolao, Giuseppe Ljung, Lennart 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 bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning bic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJF Electronics engineering::TJFM Automatic control engineering bic Book Industry Communication::P Mathematics & science::PH Physics::PHS Statistical physics bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics::PBTB Bayesian inference bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general::GPF Information theory::GPFC Cybernetics & systems theory 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. 2022-06-20T19:31:13Z 2022-06-20T19:31:13Z 2022 book ONIX_20220620_9783030958602_20 9783030958602 https://library.oapen.org/handle/20.500.12657/56998 eng Communications and Control Engineering application/pdf n/a 978-3-030-95860-2.pdf https://link.springer.com/978-3-030-95860-2 Springer Nature Springer 10.1007/978-3-030-95860-2 10.1007/978-3-030-95860-2 6c6992af-b843-4f46-859c-f6e9998e40d5 219cc0eb-31a9-46a1-a50f-c2d756c7fec1 263a7ff6-4a99-43b4-afc8-d5448fffeb8d bb7fdc2d-f519-4cb0-a902-e49e6fae9726 88d3c155-f4ad-4f96-b4a9-dc90bf36bb67 fa077fd3-081e-4681-a619-f31e3bbd3ff5 64fbf836-d99e-44f4-9a09-fcd45921f69e 0a0b0994-5cb1-435e-87b5-c81e3e5482d6 9783030958602 Springer 377 Cham [...] [...] [...] [...] [...] [...] [...] National Natural Science Foundation of China Chinese National Science Foundation open access
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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.
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