000 03751nam a22005175i 4500
001 978-1-84628-158-7
003 DE-He213
005 20161121231016.0
007 cr nn 008mamaa
008 100301s2005 xxk| s |||| 0|eng d
020 _a9781846281587
_9978-1-84628-158-7
024 7 _a10.1007/1-84628-158-X
_2doi
050 4 _aTK1-9971
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aKatayama, Tohru.
_eauthor.
245 1 0 _aSubspace Methods for System Identification
_h[electronic resource] /
_cby Tohru Katayama.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _aXVI, 392 p. 66 illus.
_bonline 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,
_x0178-5354
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.
650 0 _aEngineering.
650 0 _aChemical engineering.
650 0 _aSystem theory.
650 0 _aControl engineering.
650 0 _aElectrical engineering.
650 1 4 _aEngineering.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aControl.
650 2 4 _aSystems Theory, Control.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aIndustrial Chemistry/Chemical Engineering.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781852339814
830 0 _aCommunications and Control Engineering,
_x0178-5354
856 4 0 _uhttp://dx.doi.org/10.1007/1-84628-158-X
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c507224
_d507224