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008			181107s2019 gw a o |||| 0|eng d
020			_a9783030001933
024	7		_a10.1007/978-3-030-00193-3 _2doi
040			_bspa _aES-MaUEC _cES-MaUEC
050		4	_aQA76.38 _b2019 EB
100	1		_aLauer, Fabien _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671157
245	1	0	_aHybrid System Identification : _bTheory and Algorithms for Learning Switching Models _cby Fabien Lauer, Gérard Bloch.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019.
300			_a1 recurso en línea (XXI, 253 páginas) _b35 ilustraciones,34 ilustraciones a color
347			_atext file _bPDF
490	0		_aLecture Notes in Control and Information Sciences _x0170-8643 _v478
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction -- System Identification -- Classification -- Hybrid System Identification -- Exact Methods for Hybrid System Identification -- Estimation of Switched Linear/Affine Models -- Estimation of Piecewise Affine Models -- Recursive and State-space Identification of Hybrid Systems -- Nonlinear Hybrid System Identification.
520	3		_aHybrid System Identification helps readers to build mathematical models of dynamical systems switching between different operating modes, from their experimental observations. It provides an overview of the interaction between system identification, machine learning and pattern recognition fields in explaining and analysing hybrid system identification. It emphasises the optimization and computational complexity issues that lie at the core of the problems considered and sets them aside from standard system identification problems. The book presents practical methods that leverage this complexity, as well as a broad view of state-of-the-art machine learning methods. The authors illustrate the key technical points using examples and figures to help the reader understand the material. The book includes an in-depth discussion and computational analysis of hybrid system identification problems, moving from the basic questions of the definition of hybrid systems and system identification to methods of hybrid system identification and the estimation of switched linear/affine and piecewise affine models. The authors also give an overview of the various applications of hybrid systems, discuss the connections to other fields, and describe more advanced material on recursive, state-space and nonlinear hybrid system identification. Hybrid System Identification includes a detailed exposition of major methods, which allows researchers and practitioners to acquaint themselves rapidly with state-of-the-art tools. The book is also a sound basis for graduate and undergraduate students studying this area of control, as the presentation and form of the book provides the background and coverage necessary for a full understanding of hybrid system identification, whether the reader is initially familiar with system identification related to hybrid systems or not.
650		7	_aSistemas, Teoría de _2embne _9141328
650		7	_aModelos matemáticos _2embne _9405064
700	1		_aBloch, Gérard _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030001926
776	0	8	_iPrinted edition: _z9783030001940
776	0	8	_iPrinted edition: _z9783030130916
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-00193-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _a_alco _a_vill _b10/2019 _cm _dz _ea _feng _ggw _h0