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001 | u371150 | ||
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005 | 20160812080112.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2010 xxu| s |||| 0|eng d | ||
020 |
_a9781441906304 _9978-1-4419-0630-4 |
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040 | _cMX-MeUAM | ||
050 | 4 | _aQ295 | |
050 | 4 | _aQA402.3-402.37 | |
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[recurso electrónico] / _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 |
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337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-4419-0630-4 |
596 | _a19 | ||
942 | _cLIBRO_ELEC | ||
999 |
_c199030 _d199030 |