Sparsity Methods for Systems and Control
The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics, but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More recently...
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| Format: | Online |
| Sprog: | engelsk |
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Now Publishers
2021
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| Online adgang: | ONIX_20210420_9781680837254_5 |
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| _version_ | 1869519097972129792 |
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| author | Nagahara, Masaaki |
| author_browse | Nagahara, Masaaki |
| author_facet | Nagahara, Masaaki |
| author_sort | Nagahara, Masaaki |
| collection | Directory of Open Access Books |
| description | The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics, but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More recently, the sparsity method has been applied to systems and control to design resource-aware control systems. This book gives a comprehensive guide to sparsity methods for systems and control, from standard sparsity methods in finite-dimensional vector spaces (Part I) to optimal control methods in infinite-dimensional function spaces (Part II). The primary objective of this book is to show how to use sparsity methods for several engineering problems. For this, the author provides MATLAB programs by which the reader can try sparsity methods for themselves. Readers will obtain a deep understanding of sparsity methods by running these MATLAB programs. Sparsity Methods for Systems and Control is suitable for graduate level university courses, though it should also be comprehendible to undergraduate students who have a basic knowledge of linear algebra and elementary calculus. Also, especially part II of the book should appeal to professional researchers and engineers who are interested in applying sparsity methods to systems and control. |
| format | Online |
| id | doab-20.500.12854ir-67949 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Now Publishers |
| publisherStr | Now Publishers |
| record_format | ojs |
| spelling | doab-20.500.12854ir-679492025-03-12T18:33:19Z Sparsity Methods for Systems and Control Nagahara, Masaaki Compressed sensing, optimal control, sparse representation, convex optimization, proximal algorithms, greedy algorithms, networked control, model predictive control thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics, but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More recently, the sparsity method has been applied to systems and control to design resource-aware control systems. This book gives a comprehensive guide to sparsity methods for systems and control, from standard sparsity methods in finite-dimensional vector spaces (Part I) to optimal control methods in infinite-dimensional function spaces (Part II). The primary objective of this book is to show how to use sparsity methods for several engineering problems. For this, the author provides MATLAB programs by which the reader can try sparsity methods for themselves. Readers will obtain a deep understanding of sparsity methods by running these MATLAB programs. Sparsity Methods for Systems and Control is suitable for graduate level university courses, though it should also be comprehendible to undergraduate students who have a basic knowledge of linear algebra and elementary calculus. Also, especially part II of the book should appeal to professional researchers and engineers who are interested in applying sparsity methods to systems and control. 2021-02-10T12:58:18Z 2021-04-20T08:10:05Z 2020 book ONIX_20210420_9781680837254_5 OCN: 1258395039 https://library.oapen.org/handle/20.500.12657/47873 9781680837254 9781680837247 https://directory.doabooks.org/handle/20.500.12854/67949 eng NowOpen open access image/png image/jpeg image/jpeg image/jpeg Attribution-NonCommercial 4.0 International Attribution-NonCommercial 4.0 International Attribution-NonCommercial 4.0 International Attribution-NonCommercial 4.0 International https://library.oapen.org/bitstream/20.500.12657/47873/1/9781680837254.pdf https://library.oapen.org/bitstream/20.500.12657/47873/1/9781680837254.pdf https://library.oapen.org/bitstream/20.500.12657/47873/1/9781680837254.pdf https://library.oapen.org/bitstream/20.500.12657/47873/1/9781680837254.pdf Now Publishers Now Publishers 10.1561/9781680837254 10.1561/9781680837254 53ae8601-d009-4a47-bfed-73b89c40b091 9781680837254 9781680837247 Now Publishers 222 Norwell, MA open access |
| spellingShingle | Compressed sensing, optimal control, sparse representation, convex optimization, proximal algorithms, greedy algorithms, networked control, model predictive control thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Nagahara, Masaaki Sparsity Methods for Systems and Control |
| title | Sparsity Methods for Systems and Control |
| title_full | Sparsity Methods for Systems and Control |
| title_fullStr | Sparsity Methods for Systems and Control |
| title_full_unstemmed | Sparsity Methods for Systems and Control |
| title_short | Sparsity Methods for Systems and Control |
| title_sort | sparsity methods for systems and control |
| topic | Compressed sensing, optimal control, sparse representation, convex optimization, proximal algorithms, greedy algorithms, networked control, model predictive control thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| topic_facet | Compressed sensing, optimal control, sparse representation, convex optimization, proximal algorithms, greedy algorithms, networked control, model predictive control thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| url | ONIX_20210420_9781680837254_5 |
| work_keys_str_mv | AT nagaharamasaaki sparsitymethodsforsystemsandcontrol |