000			03633nam a22005295i 4500
001			978-0-387-35924-3
003			DE-He213
005			20161121231026.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387359243 _9978-0-387-35924-3
024	7		_a10.1007/978-0-387-35924-3 _2doi
050		4	_aQ295
050		4	_aQA402.3-402.37
072		7	_aGPFC _2bicssc
072		7	_aSCI064000 _2bisacsh
072		7	_aTEC004000 _2bisacsh
082	0	4	_a519 _223
100	1		_aDragan, Vasile. _eauthor.
245	1	0	_aMathematical Methods in Robust Control of Linear Stochastic Systems _h[electronic resource] / _cby Vasile Dragan, Toader Morozan, Adrian-Mihail Stoica.
264		1	_aNew York, NY : _bSpringer New York, _c2006.
300			_aXII, 312 p. 2 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aMathematical Concepts and Methods in Science and Engineering ; _v50
505	0		_aPreliminaries to Probability Theory and Stochastic Differential Equations -- Exponential Stability and Lyapunov-Type Linear Equations -- Structural Properties of Linear Stochastic Systems -- The Riccati Equations of Stochastic Control -- Linear Quadratic Control Problem for Linear Stochastic Systems -- Stochastic Version of the Bounded Real Lemma and Applications -- Robust Stabilization of Linear Stochastic Systems.
520			_aLinear stochastic systems are successfully used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. This monograph presents a useful methodology for the control of such stochastic systems with a focus on robust stabilization in the mean square, linear quadratic control, the disturbance attenuation problem, and robust stabilization with respect to dynamic and parametric uncertainty. Systems with both multiplicative white noise and Markovian jumping are covered. Key Features: -Covers the necessary pre-requisites from probability theory, stochastic processes, stochastic integrals and stochastic differential equations -Includes detailed treatment of the fundamental properties of stochastic systems subjected both to multiplicative white noise and to jump Markovian perturbations -Systematic presentation leads the reader in a natural way to the original results -New theoretical results accompanied by detailed numerical examples -Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations. The unique monograph is geared to researchers and graduate students in advanced control engineering, applied mathematics, mathematical systems theory and finance. It is also accessible to undergraduate students with a fundamental knowledge in the theory of stochastic systems.
650		0	_aMathematics.
650		0	_aSystem theory.
650		0	_aNumerical analysis.
650		0	_aProbabilities.
650	1	4	_aMathematics.
650	2	4	_aSystems Theory, Control.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aNumerical Analysis.
700	1		_aMorozan, Toader. _eauthor.
700	1		_aStoica, Adrian-Mihail. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387305233
830		0	_aMathematical Concepts and Methods in Science and Engineering ; _v50
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-35924-3
912			_aZDB-2-SMA
950			_aMathematics and Statistics (Springer-11649)
999			_c507482 _d507482