| 000 | 04616nam a22006615i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-95860-2 | ||
| 003 | DE-He213 | ||
| 005 | 20240828123459.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220513s2022 sz | s |||| 0|eng d | ||
| 020 |
_a9783030958602 _9978-3-030-95860-2 |
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| 024 | 7 |
_a10.1007/978-3-030-95860-2 _2doi |
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| 050 | 4 | _aQ325.5-.7 | |
| 072 | 7 |
_aUYQM _2bicssc |
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_aMAT029000 _2bisacsh |
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| 072 | 7 |
_aUYQM _2thema |
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| 082 | 0 | 4 |
_a006.31 _223 |
| 100 | 1 |
_aPillonetto, Gianluigi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _97364 |
|
| 245 | 1 | 0 |
_aRegularized System Identification _h[electronic resource] : _bLearning Dynamic Models from Data / _cby Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung. |
| 250 | _a1st ed. 2022. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
|
| 300 |
_aXXIV, 377 p. 85 illus., 73 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aCommunications and Control Engineering, _x2197-7119 |
|
| 505 | 0 | _aChapter 1. Bias -- Chapter 2. Classical System Identification -- Chapter 3. Regularization of Linear Regression Models -- Chapter 4. Bayesian Interpretation of Regularization -- Chapter 5. Regularization for Linear System Identification -- Chapter 6. Regularization in Reproducing Kernel Hilbert Spaces -- Chapter 7. Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification -- Chapter 8. Regularization for Nonlinear System Identification -- Chapter 9. Numerical Experiments and Real-World Cases. | |
| 506 | 0 | _aOpen Access | |
| 520 | _aThis 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. In many ways, this book is a complement and continuation of the much-used text book L. Ljung, System Identification, 978-0-13-656695-3. This is an open access book. | ||
| 650 | 0 | _aMachine learning. | |
| 650 | 0 |
_aControl engineering. _9618 |
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| 650 | 0 |
_aSystem theory. _9260 |
|
| 650 | 0 |
_aStatistics . _9905 |
|
| 650 | 0 | _aControl theory. | |
| 650 | 1 | 4 | _aMachine Learning. |
| 650 | 2 | 4 | _aControl and Systems Theory. |
| 650 | 2 | 4 |
_aComplex Systems. _9267 |
| 650 | 2 | 4 | _aBayesian Inference. |
| 650 | 2 | 4 |
_aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. _9910 |
| 650 | 2 | 4 | _aSystems Theory, Control. |
| 700 | 1 |
_aChen, Tianshi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _97371 |
|
| 700 | 1 |
_aChiuso, Alessandro. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _97372 |
|
| 700 | 1 |
_aDe Nicolao, Giuseppe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _97373 |
|
| 700 | 1 |
_aLjung, Lennart. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _97374 |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030958596 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030958619 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030958626 |
| 830 | 0 |
_aCommunications and Control Engineering, _x2197-7119 _97375 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-95860-2 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 912 | _aZDB-2-SOB | ||
| 999 |
_c572538 _d572538 |
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