000			03548nam a22004935i 4500
001			978-1-84628-178-5
003			DE-He213
005			20161121231016.0
007			cr nn 008mamaa
008			100301s2005 xxk| s |||| 0|eng d
020			_a9781846281785 _9978-1-84628-178-5
024	7		_a10.1007/1-84628-178-4 _2doi
050		4	_aTJ212-225
072		7	_aTJFM _2bicssc
072		7	_aTEC004000 _2bisacsh
082	0	4	_a629.8 _223
245	1	0	_aModelling and Identification with Rational Orthogonal Basis Functions _h[electronic resource] / _cedited by Peter S.C. Heuberger, Paul M.J. Van den Hof, Bo Wahlberg.
264		1	_aLondon : _bSpringer London, _c2005.
300			_aXXVI, 397 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aConstruction and Analysis -- Transformation Analysis -- System Identification with Generalized Orthonormal Basis Functions -- Variance Error, Reproducing Kernels, and Orthonormal Bases -- Numerical Conditioning -- Model Uncertainty Bounding -- Frequency-domain Identification in ?2 -- Frequency-domain Identification in ?? -- Design Issues -- Pole Selection in GOBF Models -- Transformation Theory -- Realization Theory.
520			_aModels of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science. A model is always only an approximation of a real phenomenon so that having an approximation theory which allows for the analysis of model quality is a substantial concern. The use of rational orthogonal basis functions to represent dynamical systems and stochastic signals can provide such a theory and underpin advanced analysis and efficient modelling. It also has the potential to extend beyond these areas to deal with many problems in circuit theory, telecommunications, systems, control theory and signal processing. Nine international experts have contributed to this work to produce thirteen chapters that can be read independently or as a comprehensive whole with a logical line of reasoning: • Construction and analysis of generalized orthogonal basis function model structure; • System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding; • System identification in the frequency domain; • Design issues and optimal basis selection; • Transformation and realization theory. Modelling and Identification with Rational Orthogonal Basis Functions affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.
650		0	_aEngineering.
650		0	_aComputer simulation.
650		0	_aSystem theory.
650		0	_aControl engineering.
650	1	4	_aEngineering.
650	2	4	_aControl.
650	2	4	_aSystems Theory, Control.
650	2	4	_aSimulation and Modeling.
650	2	4	_aSignal, Image and Speech Processing.
700	1		_aHeuberger, Peter S.C. _eeditor.
700	1		_aHof, Paul M.J. Van den. _eeditor.
700	1		_aWahlberg, Bo. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781852339562
856	4	0	_uhttp://dx.doi.org/10.1007/1-84628-178-4
912			_aZDB-2-ENG
950			_aEngineering (Springer-11647)
999			_c507225 _d507225