Stochastic Control of Hereditary Systems and Applications

This research monograph develops the Hamilton-Jacobi-Bellman (HJB) theory through dynamic programming principle for a class of optimal control problems for stochastic hereditary differential systems. It is driven by a standard Brownian motion and with a bounded memory or an infinite but fading memor...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Chang, Mou-Hsiung (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Stochastic Modelling and Applied Probability, 59
Temas:
Probabilities.
Differential equations.
Control engineering.
Robotics.
Automation.
Statistics .
Probability Theory.
Differential Equations.
Control, Robotics, Automation.
Statistical Theory and Methods.
Acceso en línea:Texto Completo
Descripción
Sumario:This research monograph develops the Hamilton-Jacobi-Bellman (HJB) theory through dynamic programming principle for a class of optimal control problems for stochastic hereditary differential systems. It is driven by a standard Brownian motion and with a bounded memory or an infinite but fading memory. The optimal control problems treated in this book include optimal classical control and optimal stopping with a bounded memory and over finite time horizon. This book can be used as an introduction for researchers and graduate students who have a special interest in learning and entering the research areas in stochastic control theory with memories. Each chapter contains a summary. Mou-Hsiung Chang is a program manager at the Division of Mathematical Sciences for the U.S. Army Research Office.
Descripción Física:XVIII, 406 p. online resource.
ISBN:9780387758169
ISSN:2197-439X ;

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