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710	2		_aSpringerLink (Online service) _9106996
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008			180803s2019 gw a o |||| 0|eng d
020			_a9783319896205
024	7		_a10.1007/978-3-319-89620-5 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA221 _b2019 EB
100	1		_aMarkovsky, Ivan _eautor _9670896
245	1	0	_aLow-rank Approximation : _bAlgorithms, Implementation, Applications _cby Ivan Markovsky
250			_aSecond edition
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (XIII, 272 páginas) _b19 ilustraciones, 15 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aCommunications and Control Engineering _x0178-5354
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aChapter 1. Introduction -- Part I: Linear modeling problems -- Chapter 2. From data to models -- Chapter 3. Exact modelling -- Chapter 4. Approximate modelling -- Part II: Applications and generalizations -- Chapter 5. Applications -- Chapter 6. Data-driven filtering and control -- Chapter 7. Nonlinear modeling problems -- Chapter 8. Dealing with prior knowledge -- Index. .
520	3		_aThis book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory with a range of applications from systems and control theory to psychometrics being described. Special knowledge of the application fields is not required. The second edition of /Low-Rank Approximation/ is a thoroughly edited and extensively rewritten revision. It contains new chapters and sections that introduce the topics of: • variable projection for structured low-rank approximation; • missing data estimation; • data-driven filtering and control; • stochastic model representation and identification; • identification of polynomial time-invariant systems; and • blind identification with deterministic input model. The book is complemented by a software implementation of the methods presented, which makes the theory directly applicable in practice. In particular, all numerical examples in the book are included in demonstration files and can be reproduced by the reader. This gives hands-on experience with the theory and methods detailed. In addition, exercises and MATLAB^® /Octave examples will assist the reader quickly to assimilate the theory on a chapter-by-chapter basis. "Each chapter is completed with a new section of exercises to which complete solutions are provided." Low-Rank Approximation (second edition) is a broad survey of the Low-Rank Approximation theory and applications of its field which will be of direct interest to researchers in system identification, control and systems theory, numerical linear algebra and optimization. The supplementary problems and solutions render it suitable for use in teaching graduate courses in those subjects as well.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aAproximación, Teoría de la _9160958
650		7	_2embne _9140864 _aÁlgebra
776	0	8	_iPrinted edition: _z9783319896199
776	0	8	_iPrinted edition: _z9783319896212
776	0	8	_iPrinted edition: _z9783030078171
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-89620-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b09/2019 _eel _zSI