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008			150903s2005 xxk| o |||| 0|eng d
020			_a9781846281587 _99781846281587
024	7		_a10.1007/184628158-X _2doi
035			_avtls000343700
039		9	_a201509030748 _bVLOAD _c201404120950 _dVLOAD _c201404090728 _dVLOAD _y201402061202 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
100	1		_aKatayama, Tohru. _eautor _9323019
245	1	0	_aSubspace Methods for System Identification / _cby Tohru Katayama.
264		1	_aLondon : _bSpringer London, _c2005.
300			_axvI, 392 páginas 66 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aCommunications and Control Engineering, _x0178-5354
500			_aSpringer eBooks
505	0		_aPreliminaries -- Linear Algebra and Preliminaries -- Discrete-Time Linear Systems -- Stochastic Processes -- Kalman Filter -- Realization Theory -- Realization of Deterministic Systems -- Stochastic Realization Theory (1) -- Stochastic Realization Theory (2) -- Subspace Identification -- Subspace Identification (1) — ORT -- Subspace Identification (2) — CCA -- Identification of Closed-loop System.
520			_aSystem identification provides methods for the sensible approximation of real systems using a model set based on experimental input and output data. Tohru Katayama sets out an in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results. The text is structured into three parts. First, the mathematical preliminaries are dealt with: numerical linear algebra; system theory; stochastic processes; and Kalman filtering. The second part explains realization theory, particularly that based on the decomposition of Hankel matrices, as it is applied to subspace identification methods. Two stochastic realization results are included, one based on spectral factorization and Riccati equations, the other on canonical correlation analysis (CCA) for stationary processes. Part III uses the development of stochastic realization results, in the presence of exogenous inputs, to demonstrate the closed-loop application of subspace identification methods CCA and ORT (based on orthogonal decomposition). The addition of tutorial problems with solutions and Matlab® programs which demonstrate various aspects of the methods propounded to introductory and research material makes Subspace Methods for System Identification not only an excellent reference for researchers but also a very useful text for tutors and graduate students involved with courses in control and signal processing. The book can be used for self-study and will be of much interest to the applied scientist or engineer wishing to use advanced methods in modeling and identification of complex systems.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781852339814
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-84628-158-X _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c291630 _d291630