Stochastic Control of Hereditary Systems and Applications (eBook, PDF)
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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 resear...
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.
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.
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