Stochastic Modeling And Control
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Stochastic Modeling And Control
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Author : Ivan Ivanov
language : en
Publisher: BoD – Books on Demand
Release Date : 2012-11-28
Stochastic Modeling And Control written by Ivan Ivanov and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-28 with Mathematics categories.
Stochastic control plays an important role in many scientific and applied disciplines including communications, engineering, medicine, finance and many others. It is one of the effective methods being used to find optimal decision-making strategies in applications. The book provides a collection of outstanding investigations in various aspects of stochastic systems and their behavior. The book provides a self-contained treatment on practical aspects of stochastic modeling and calculus including applications drawn from engineering, statistics, and computer science. Readers should be familiar with basic probability theory and have a working knowledge of stochastic calculus. PhD students and researchers in stochastic control will find this book useful.
Stochastic Modelling And Control
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Author : Mark Davis
language : en
Publisher: Springer
Release Date : 2012-02-13
Stochastic Modelling And Control written by Mark Davis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-13 with Science categories.
This book aims to provide a unified treatment of input/output modelling and of control for discrete-time dynamical systems subject to random disturbances. The results presented are of wide applica bility in control engineering, operations research, econometric modelling and many other areas. There are two distinct approaches to mathematical modelling of physical systems: a direct analysis of the physical mechanisms that comprise the process, or a 'black box' approach based on analysis of input/output data. The second approach is adopted here, although of course the properties ofthe models we study, which within the limits of linearity are very general, are also relevant to the behaviour of systems represented by such models, however they are arrived at. The type of system we are interested in is a discrete-time or sampled-data system where the relation between input and output is (at least approximately) linear and where additive random dis turbances are also present, so that the behaviour of the system must be investigated by statistical methods. After a preliminary chapter summarizing elements of probability and linear system theory, we introduce in Chapter 2 some general linear stochastic models, both in input/output and state-space form. Chapter 3 concerns filtering theory: estimation of the state of a dynamical system from noisy observations. As well as being an important topic in its own right, filtering theory provides the link, via the so-called innovations representation, between input/output models (as identified by data analysis) and state-space models, as required for much contemporary control theory.
Stochastic Modelling And Control
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Author : M. H. A. Davis
language : en
Publisher: Springer
Release Date : 1985-05-16
Stochastic Modelling And Control written by M. H. A. Davis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-05-16 with Juvenile Nonfiction categories.
This book aims to provide a unified treatment of input/output modelling and of control for discrete-time dynamical systems subject to random disturbances. The results presented are of wide applica bility in control engineering, operations research, econometric modelling and many other areas. There are two distinct approaches to mathematical modelling of physical systems: a direct analysis of the physical mechanisms that comprise the process, or a 'black box' approach based on analysis of input/output data. The second approach is adopted here, although of course the properties ofthe models we study, which within the limits of linearity are very general, are also relevant to the behaviour of systems represented by such models, however they are arrived at. The type of system we are interested in is a discrete-time or sampled-data system where the relation between input and output is (at least approximately) linear and where additive random dis turbances are also present, so that the behaviour of the system must be investigated by statistical methods. After a preliminary chapter summarizing elements of probability and linear system theory, we introduce in Chapter 2 some general linear stochastic models, both in input/output and state-space form. Chapter 3 concerns filtering theory: estimation of the state of a dynamical system from noisy observations. As well as being an important topic in its own right, filtering theory provides the link, via the so-called innovations representation, between input/output models (as identified by data analysis) and state-space models, as required for much contemporary control theory.
Stochastic Modeling And Control
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Author : Jacek Jakubowski
language : en
Publisher:
Release Date : 2020
Stochastic Modeling And Control written by Jacek Jakubowski and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Stochastic Modeling Of Manufacturing Systems
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Author : George Liberopoulos
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-12
Stochastic Modeling Of Manufacturing Systems written by George Liberopoulos and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-12 with Business & Economics categories.
Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events, such as order changes, equipment failures and product defects, affect the performance of the system and complicate decision-making. This volume is devoted to the development of analytical methods aiming at responding to variability in a way that limits its corrupting effects on system performance. The book includes fifteen novel chapters that mostly focus on the development and analysis of performance evaluation models of manufacturing systems using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints: factory design, unreliable production lines, queuing network models, production planning and assembly.
Optimal Stochastic Modeling And Control Of Flexible Structures
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Author :
language : en
Publisher:
Release Date : 1988
Optimal Stochastic Modeling And Control Of Flexible Structures written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with categories.
Modeling and control design of large flexible space structures under high uncertainty is considered. Linear stochastic system models with multiplicative and additive noises that are state -, control -, and measurement - dependent are treated extensively. Controllability, observability, and robustness issues are also discussed under various optimality considerations. An optimal stochastic controller is derived under perfect information and a sub- optimal compensator is formulated under partial and noisy information. The intent of the report is to treat the modeling of uncertainties and to develop the appropriate stochastic control that is robust. Aircraft; Jet aircraft; Linear control systems; Stochastic systems; Optimal control; Estimation; Robust control systems; Stochastic discreet time system; Multi-variable control.
Stochastic Models Estimation And Control
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Author : Peter S. Maybeck
language : en
Publisher: Academic Press
Release Date : 1982-08-25
Stochastic Models Estimation And Control written by Peter S. Maybeck and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982-08-25 with Mathematics categories.
This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.
Applied Stochastic Models And Control For Finance And Insurance
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Author : Charles S. Tapiero
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Applied Stochastic Models And Control For Finance And Insurance written by Charles S. Tapiero and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
Applied Stochastic Models and Control for Finance and Insurance presents at an introductory level some essential stochastic models applied in economics, finance and insurance. Markov chains, random walks, stochastic differential equations and other stochastic processes are used throughout the book and systematically applied to economic and financial applications. In addition, a dynamic programming framework is used to deal with some basic optimization problems. The book begins by introducing problems of economics, finance and insurance which involve time, uncertainty and risk. A number of cases are treated in detail, spanning risk management, volatility, memory, the time structure of preferences, interest rates and yields, etc. The second and third chapters provide an introduction to stochastic models and their application. Stochastic differential equations and stochastic calculus are presented in an intuitive manner, and numerous applications and exercises are used to facilitate their understanding and their use in Chapter 3. A number of other processes which are increasingly used in finance and insurance are introduced in Chapter 4. In the fifth chapter, ARCH and GARCH models are presented and their application to modeling volatility is emphasized. An outline of decision-making procedures is presented in Chapter 6. Furthermore, we also introduce the essentials of stochastic dynamic programming and control, and provide first steps for the student who seeks to apply these techniques. Finally, in Chapter 7, numerical techniques and approximations to stochastic processes are examined. This book can be used in business, economics, financial engineering and decision sciences schools for second year Master's students, as well as in a number of courses widely given in departments of statistics, systems and decision sciences.
Modeling Analysis Design And Control Of Stochastic Systems
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Author : V. G. Kulkarni
language : en
Publisher: Springer
Release Date : 2014-01-13
Modeling Analysis Design And Control Of Stochastic Systems written by V. G. Kulkarni and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-13 with Technology & Engineering categories.
This is an introductory level text on stochastic modeling. It is suited for undergraduate or graduate students in actuarial science, business management, computer science, engineering, operations research, public policy, statistics, and mathematics. It employs a large number of examples to teach how to build stochastic models of physical systems, analyze these models to predict their performance, and use the analysis to design and control them. The book provides a self-contained review of the relevant topics in probability theory. The rest of the book is devoted to important classes of stochastic models. In discrete and continuous time Markov models it covers the transient and long term behavior, cost models, and first passage times. Under generalized Markov models, it covers renewal processes, cumulative processes and semi-Markov processes. All the material is illustrated with many examples. There is a separate chapter on queueing models. In the chapter on design the author shows how the techniques developed in the text can be used to optimize the performance of a system. Finally, in the last chapter, linear programming is used to compute optimal control policies for stochastic systems. The book emphasizes numerical answers to the problems. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Craolina at Chapel Hill. He has authored a graduate level text 'Modeling and Analysis of Stochastic Systems' and research articles on stochastic models of queues, computer systems and telecommunication systems. He holds a patent on traffic management in telecommunication networks, and he has served as an editor and associate editor of Stochastic Models and Operations Research Letters.
Stochastic Modeling And Control Of Neural And Small Length Scale Dynamical Systems
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Author : Gautam Kumar
language : en
Publisher:
Release Date : 2013
Stochastic Modeling And Control Of Neural And Small Length Scale Dynamical Systems written by Gautam Kumar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Recent advancements in experimental and computational techniques have created tremendous opportunities in the study of fundamental questions of science and engineering by taking the approach of stochastic modeling and control of dynamical systems. Examples include but are not limited to neural coding and emergence of behaviors in biological networks. Integrating optimal control strategies with stochastic dynamical models has ignited the development of new technologies in many emerging applications. In this direction, particular examples are brain-machine interfaces (BMIs), and systems to manipulate submicroscopic objects. The focus of this dissertation is to advance these technologies by developing optimal control strategies under various feedback scenarios and system uncertainties.