| 000 | 02886naaaa2200481uu 4500 | ||
|---|---|---|---|
| 003 | BUT | ||
| 005 | 20230526093427.0 | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20210210s2020 maux |||||o ||||eng|| d | ||
| 020 | _a9781680837254 | ||
| 020 | _a9781680837254 | ||
| 020 | _a9781680837247 | ||
| 024 | 7 |
_a10.1561/9781680837254 _cdoi |
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| 040 |
_aoapen _coapen |
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| 041 | 0 | _aeng | |
| 042 | _adc | ||
| 080 | _a004.8 | ||
| 100 | 1 |
_aNagahara, Masaaki _4auth |
|
| 245 | 1 | 0 | _aSparsity Methods for Systems and Control |
| 260 |
_aNorwell, MA _bNow Publishers _c2020 |
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| 300 | _a1 electronic resource (222 p.) | ||
| 490 | 1 | _aNowOpen | |
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aThe 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. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by-nc/4.0/ _2cc |
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| 546 | _aEnglish | ||
| 650 | 0 |
_aИскусственный интеллект _94518 |
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| 653 | _aCompressed sensing | ||
| 653 | _aOptimal control | ||
| 653 | _aSparse representation | ||
| 653 | _aConvex optimization | ||
| 653 | _aProximal algorithms | ||
| 653 | _aGreedy algorithms | ||
| 653 | _aNetworked control | ||
| 653 | _aModel predictive control | ||
| 653 | _aArtificial intelligence | ||
| 830 |
_94532 _aNowOpen |
||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://library.oapen.org/bitstream/20.500.12657/47873/1/9781680837254.pdf _70 _zDownload |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/67949 _70 _zDescription |
| 909 |
_c197 _dKhurliman Arzieva |
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| 942 |
_2udc _cEE |
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| 999 |
_c6624 _d6624 |
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