Regularized System Identification
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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| Hoofdauteurs: | , , , , |
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| Formaat: | Online |
| Taal: | Engels |
| Gepubliceerd in: |
Springer Nature
2022
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| Onderwerpen: | |
| Online toegang: | ONIX_20220620_9783030958602_20 |
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| _version_ | 1869515247637757952 |
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| author | Pillonetto, Gianluigi Chen, Tianshi Chiuso, Alessandro De Nicolao, Giuseppe Ljung, Lennart |
| author_browse | Chen, Tianshi Chiuso, Alessandro De Nicolao, Giuseppe Ljung, Lennart Pillonetto, Gianluigi |
| author_facet | Pillonetto, Gianluigi Chen, Tianshi Chiuso, Alessandro De Nicolao, Giuseppe Ljung, Lennart |
| author_sort | Pillonetto, Gianluigi |
| collection | Directory of Open Access Books |
| 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. |
| format | Online |
| id | doab-20.500.12854ir-84390 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-843902025-03-16T02:58:45Z 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 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. 2022-06-21T04:03:27Z 2022-06-21T04:03:27Z 2022-06-20T19:31:13Z 2022 book ONIX_20220620_9783030958602_20 OCN: 1323245602 https://library.oapen.org/handle/20.500.12657/56998 9783030958602 https://directory.doabooks.org/handle/20.500.12854/84390 eng Communications and Control Engineering open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/56998/1/978-3-030-95860-2.pdf https://library.oapen.org/bitstream/20.500.12657/56998/1/978-3-030-95860-2.pdf https://library.oapen.org/bitstream/20.500.12657/56998/1/978-3-030-95860-2.pdf Springer Nature Springer 10.1007/978-3-030-95860-2 10.1007/978-3-030-95860-2 9fa3421d-f917-4153-b9ab-fc337c396b5a National Natural Science Foundation of China 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 [...] [...] [...] [...] [...] [...] [...] open access |
| spellingShingle | 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 Pillonetto, Gianluigi Chen, Tianshi Chiuso, Alessandro De Nicolao, Giuseppe Ljung, Lennart Regularized System Identification |
| title | Regularized System Identification |
| title_full | Regularized System Identification |
| title_fullStr | Regularized System Identification |
| title_full_unstemmed | Regularized System Identification |
| title_short | Regularized System Identification |
| title_sort | regularized system identification |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20220620_9783030958602_20 |
| work_keys_str_mv | AT pillonettogianluigi regularizedsystemidentification AT chentianshi regularizedsystemidentification AT chiusoalessandro regularizedsystemidentification AT denicolaogiuseppe regularizedsystemidentification AT ljunglennart regularizedsystemidentification |