000			03783nam a22005055i 4500
001			978-0-387-31279-8
003			DE-He213
005			20250710083948.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387312798 _a99780387312798
024	7		_a10.1007/b120940 _2doi
082	0	4	_a519 _223
100	1		_aChornei, Ruslan K. _eauthor.
245	1	0	_aControl of Spatially Structured Random Processes and Random Fields with Applications _h[recurso electrónico] / _cby Ruslan K. Chornei, Hans Daduna, Pavel S. Knopov.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_aXIV, 262 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aNonconvex Optimization and Its Applications, _x1571-568X ; _v86
505	0		_aPrerequisites from the Theory of Stochastic Processes and Stochastic Dynamic Optimization -- Local Control of Discrete Time Interacting Markov Processes with Graph Structured State Space -- Sequential Stochastic Games with Distributed Players on Graphs -- Local Control of Continuous Time Interacting Markov and Semi-Markov Processes with Graph Structured State Space -- Connections with Optimization of Random Field in Different Areas.
520			_aThis book is devoted to the study and optimization of spatiotemporal stochastic processes, that is, processes which develop simultaneously in space and time under random influences. These processes are seen to occur almost everywhere when studying the global behavior of complex systems, including • physical and technical systems, • population dynamics, • neural networks, • computer and telecommunication networks, • complex production networks, • flexible manufacturing systems, • logistic networks and transportation systems, • environmental engineering, • climate modelling and prediction, • earth surface models. Classical stochastic dynamic optimization forms the framework of the book. Taken as a whole, the project undertaken in the book is to establish optimality or near-optimality for Markovian policies in the control of spatiotemporal Markovian processes. The authors apply this general principle to different frameworks of Markovian systems and processes. Depending on the structure of the systems and the surroundings of the model classes the authors arrive at different levels of simplicity for the policy classes which encompass optimal or nearly optimal policies. A set of examples accompanies the theoretical findings, and these examples should demonstrate some important application areas for the theorems discussed. Audience This book is intended for experts in applied mathematics, cybernetics, and in the theory of optimal control.
650		0	_aMATHEMATICS.
650		0	_aSYSTEMS THEORY.
650		0	_aOPERATIONS RESEARCH.
650		0	_aDISTRIBUTION (PROBABILITY THEORY).
650	1	4	_aMATHEMATICS.
650	2	4	_aSYSTEMS THEORY, CONTROL.
650	2	4	_aAPPLICATIONS OF MATHEMATICS.
650	2	4	_aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
650	2	4	_aOPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING.
650	2	4	_aGAME THEORY, ECONOMICS, SOCIAL AND BEHAV. SCIENCES.
700	1		_aDaduna, Hans. _eauthor.
700	1		_aKnopov, Pavel S. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387304090
830		0	_aNonconvex Optimization and Its Applications, _x1571-568X ; _v86
856	4	0	_uhttp://dx.doi.org/10.1007/b120940 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c57133 _d57133