000			03697nam a22005655i 4500
001			978-1-4419-0630-4
003			DE-He213
005			20140220084502.0
007			cr nn 008mamaa
008			100301s2010 xxu| s |||| 0|eng d
020			_a9781441906304 _9978-1-4419-0630-4
024	7		_a10.1007/978-1-4419-0630-4 _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 Discrete-Time Linear Stochastic Systems _h[electronic resource] / _cby Vasile Dragan, Toader Morozan, Adrian-Mihail Stoica.
250			_aFirst.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aElements of probability theory -- Discrete-time linear equations defined by positive operators -- Mean square exponential stability -- Structural properties of linear stochastic systems -- Discrete-time Riccati equations of stochastic control -- Linear quadratic optimization problems -- Discrete-time stochastic optimal control -- Robust stability and robust stabilization of discrete-time linear stochastic systems.
520			_aIn this monograph the authors develop a theory for the robust control of discrete-time stochastic systems, subjected to both independent random perturbations and to Markov chains. Such systems are widely used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. The theory is a continuation of the authors’ work presented in their previous book entitled "Mathematical Methods in Robust Control of Linear Stochastic Systems" published by Springer in 2006. Key features: - Provides a common unifying framework for discrete-time stochastic systems corrupted with both independent random perturbations and with Markovian jumps which are usually treated separately in the control literature - Covers preliminary material on probability theory, independent random variables, conditional expectation and Markov chains - Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations - Leads the reader in a natural way to the original results through a systematic presentation - Presents new theoretical results with detailed numerical examples The 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	_aFunctional equations.
650		0	_aSystems theory.
650		0	_aNumerical analysis.
650		0	_aMathematical optimization.
650		0	_aDistribution (Probability theory).
650	1	4	_aMathematics.
650	2	4	_aSystems Theory, Control.
650	2	4	_aOptimization.
650	2	4	_aNumerical Analysis.
650	2	4	_aDifference and Functional Equations.
650	2	4	_aProbability Theory and Stochastic Processes.
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: _z9781441906298
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4419-0630-4
912			_aZDB-2-SMA
999			_c110227 _d110227