by North-Holland Pub. Co., Sole distributors for the U.S.A. and Canada, Elsevier North-Holland in Amsterdam, New York, New York .
Written in English
Bibliography: p. 399-410.
|Series||Studies in mathematics and its applications ;, v. 11|
|LC Classifications||QA402.3 .B433 1982|
|The Physical Object|
|Pagination||xv, 410 p. :|
|Number of Pages||410|
|LC Control Number||81019900|
Get this from a library! Stochastic control by functional analysis methods. [Alain Bensoussan]. Purchase Stochastic Control by Functional Analysis Methods, Volume 11 - 1st Edition. Print Book & E-Book. ISBN , Pages: Genre/Form: Electronic books: Additional Physical Format: Print version: Bensoussan, Alain. Stochastic control by functional analysis methods. Amsterdam ; New York. This book is concerned with numerical methods for stochastic control and optimal stochastic control problems. The random process models of the controlled or uncontrolled stochastic systems are either diffusions or jump diffusions. Stochastic control is a very active area of research and new prob lem formulations and sometimes surprising applications appear regularly.
This book is written for engineers; therefore, it avoids measure theory, functional analysis, and other disciplines that may not be in an engineers background. The book is self-contained with the objective to investigate the theory and derive from it the tools required to reach the ultimate objective, generating practical designs for estimators. In probability theory and related fields, a stochastic or random process is a mathematical object usually defined as a family of random ically, the random variables were associated with or indexed by a set of numbers, usually viewed as points in time, giving the interpretation of a stochastic process representing numerical values of some system randomly changing over time, such. Maximum Element Stochastic Control Admissible Control Stochastic Control Problem Standard Wiener Process These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves. Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system. The system designer assumes, in a Bayesian probability-driven fashion, that random noise with known probability distribution affects the evolution and observation of the state variables.
This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures. This 3rd edition provides an insight into the mathematical crossroads formed by functional analysis (the macroscopic approach), partial differential equations (the mesoscopic. Abstract. This paper is concerned with methods used for state estimation and control of stochastic nonlinear systems. Approaches to lumped parameter systems and distributed ones are distinguished and specific features concerning system structures, state estimation and optimal control are briefly reviewed and discussed from viewpoints of both possible advantages and difficulties for. The prerequisites are: standard functional analysis, the theory of semigroups of operators and its use in the study of PDEs, some knowledge of the dynamic programming approach to stochastic optimal control problems in finite dimension, and the basics of stochastic analysis and stochastic equations in infinite-dimensional spaces. class of interesting models, and to developsome stochastic control and ltering theory in the most basic setting. Stochastic integration with respect to general semimartin-gales, and many other fascinating (and useful) topics, are left for a more advanced course. Similarly, the stochastic control portion of these notes concentrates on veri-.