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008			220513s2022 sz a o |||| 0|eng d
020			_a9783030958602
024	7		_a10.1007/978-3-030-95860-2 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA402 _b2022 EB
100	1		_aPillonetto, Gianluigi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685484
245	1	0	_aRegularized System Identification : _bLearning Dynamic Models from Data _cby Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXIV, 377 páginas) _b85 ilustraciones, 73 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9138446 _aAnálisis de sistemas
700	1		_aChin, Tenji _d1971- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685485
700	1		_aChiuso, Alessandro _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685486
700	1		_aDe Nicolao, Giuseppe _d1962- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685487
700	1		_aLjung, Lennart _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685488
776	0	8	_iPrinted edition: _z9783030958596
776	0	8	_iPrinted edition: _z9783030958619
776	0	8	_iPrinted edition: _z9783030958626
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-95860-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eb _zSI