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020			_a9783030958602
024	7		_a10.1007/978-3-030-95860-2 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aQA402
072		7	_aUYQM _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQM _2thema
090			_aQA402EBK
100	1		_aPillonetto, Gianluigi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
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			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
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.
650		0	_aSystem theory.
650		0	_aStatistics .
650		0	_aControl theory.
650	1	4	_aMachine Learning.
650	2	4	_aControl and Systems Theory.
650	2	4	_aComplex Systems.
650	2	4	_aBayesian Inference.
650	2	4	_aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650	2	4	_aSystems Theory, Control .
653		0	_aSystem identification
700	1		_aChen, Tianshi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aChiuso, Alessandro. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aDe Nicolao, Giuseppe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aLjung, Lennart. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
830		0	_aCommunications and Control Engineering, _x2197-7119
856	4	0	_uhttps://doi.org/10.1007/978-3-030-95860-2 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK