Details
Control of Spatially Structured Random Processes and Random Fields with Applications
Nonconvex Optimization and Its Applications, Band 86
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96,29 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 03.09.2006 |
| ISBN/EAN: | 9780387312798 |
| Sprache: | englisch |
| Anzahl Seiten: | 262 |
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Beschreibungen
This book is devoted to the study and optimization of spatiotem- ral stochastic processes. These are processes that simultaneously - velop in space and time under random influences. Such processes - cur almost everywhere when the global behavior of complex systems is studied, e.g., in physical and technical systems, population dynamics, neural networks, computer and telecommunication networks, complex production networks and flexible manufacturing systems, logistic n- works and transportation systems, environmental engineering, climate modeling and prediction, earth surface models, and so on. In the study of spatiotemporal stochastic processes the classical c- cepts of random fields (which are models for spatially distributed r- dom phenomena) and of stochastic processes (hich are usually thought to describe the evolution over time of systems under random influences) converge. Over the last twenty years,many research monographs were written with emphasis on this unifying point of view, as were a huge number of articles and papers on this subject.
Prerequisites 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.
<P>This book is devoted to the study and optimization of <EM>spatiotemporal stochastic processes</EM>, 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 </P>
<P> • physical and technical systems, </P>
<P> • population dynamics, </P>
<P> • neural networks, </P>
<P> • computer and telecommunication networks, </P>
<P> • complex production networks, </P>
<P> • flexible manufacturing systems, </P>
<P> • logistic networks and transportation systems, </P>
<P> • environmental engineering, </P>
<P> • climate modelling and prediction, </P>
<P> • earth surface models. </P>
<P>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.</P>
<P><EM>Audience</EM></P>
<P>This book is intended for experts in applied mathematics, cybernetics, and in the theory of optimal control.</P>
<P> • physical and technical systems, </P>
<P> • population dynamics, </P>
<P> • neural networks, </P>
<P> • computer and telecommunication networks, </P>
<P> • complex production networks, </P>
<P> • flexible manufacturing systems, </P>
<P> • logistic networks and transportation systems, </P>
<P> • environmental engineering, </P>
<P> • climate modelling and prediction, </P>
<P> • earth surface models. </P>
<P>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.</P>
<P><EM>Audience</EM></P>
<P>This book is intended for experts in applied mathematics, cybernetics, and in the theory of optimal control.</P>
Presents problems and content not considered in other books on controlled Markov processes, especially regarding controlled Markov fields on graphs Includes supplementary material: sn.pub/extras
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