Ebook: Stochastic Control of Hereditary Systems and Applications
Author: Mou-Hsiung Chang (eds.)
- Genre: Mathematics // Probability
- Tags: Probability Theory and Stochastic Processes, Partial Differential Equations, Control Robotics Mechatronics, Statistical Theory and Methods
- Series: Stochastic modelling and applied probability 59
- Year: 2008
- Publisher: Springer-Verlag New York
- City: New York
- Edition: 1
- Language: English
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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 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.
This research monograph develops the Hamilton-Jacobi-Bellman theory via dynamic programming principle for a class of optimal control problems for stochastic hereditary differential equations (SHDEs) driven by a standard Brownian motion and with a bounded or an infinite but fading memory. These equations represent a class of stochastic infinite-dimensional systems that become increasingly important and have wide range of applications in physics, chemistry, biology, engineering and economics/finance.