stochastic processes estimation and control

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Stochastic Processes Estimation And Control

Author : Jason L. Speyer
ISBN : 9780898716559
Genre : Mathematics
File Size : 71. 58 MB
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The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter aswell as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to HÝsubscript 2¨ and HÝsubscript Ýinfinity¨¨ controllers and system robustness. This book is suitable for first-year graduate students in electrical, mechanical, chemical, and aerospace engineering specializing in systems and control. Students in computer science, economics, and possibly business will also find it useful.

Stochastic Processes Estimation And Control

Author : Jason L. Speyer
ISBN : 9780898718591
Genre : Control theory
File Size : 89. 49 MB
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Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities.

Stochastic Processes Estimation And Control

Author : George N. Saridis
ISBN : UOM:39015034252653
Genre : Mathematics
File Size : 83. 55 MB
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The first new introduction to stochastic processes in 20 years incorporates a modern, innovative approach to estimation and control theory Stochastic Processes, Estimation, and Control: The Entropy Approach provides a comprehensive, up-to-date introduction to stochastic processes, together with a concise review of probability and system theory. Serving both as a text for graduate courses in control and systems engineering and as a guide for the practicing engineer and researcher, the book applies the most recent advances in stochastic and probabilistic processes to such areas as communications systems and robotics technology. Highlights of this outstanding text: Uses the thermodynamic principle of entropy as a tool with which to measure and analyze uncertainty in systems-a feature unique to this text Covers basic material on stochastic processes, parameter and state estimation and control with a novel approach that does not require advanced mathematics Introduces an original approach to the design of nonlinear stochastic control that converges sequentially to the optimal-a practical method that will be particularly appealing to the practicing engineer Reformulates the stochastic estimation and control problem from a global point of view and introduces entropy as the entity that defines control theory as an energy product-a groundbreaking approach Since the early 18th century, scientists have devised probability measures to help them describe uncertain phenomena and approximate the actual laws of nature. It was not until the advent of the computer, however, that stochastic processes have made it possible to approximate the uncertainty inherent in large complex systems. The miracles of space exploration, modern defense systems, and artificial intelligence are just a few of the dramatic advances that have come about due to the ability of the modern digital computer to perform massive probabilistic calculations. In this, the first introductory book on stochastic processes in twenty years, leading theoretician George Saridis provides a modern innovative approach that applies the most recent advances in probabilistic processes to such areas as communications and robotics technology. Stochastic Processes, Estimation, and Control: The Entropy Approach is designed as a text for graduate courses in dynamic programming and stochastic control, stochastic processes, or applied probability in the engineering or mathematical/computational science departments, and as a guide for the practicing engineer and researcher. It offers a lucid discussion of parameter estimation based on least square techniques, an in-depth investigation of the estimation of the states of a stochastic linear and nonlinear dynamic system, and a modified derivation of the linear-quadratic Gaussian optimal control problem. Professor Saridis's presentation of estimation and control theory is thorough, but avoids the use of advanced mathematics. A new theory of approximation of the optimal solution for nonlinear stochastic systems is presented as a general engineering tool, and the whole area of stochastic processes, estimation, and control is recast using entropy as a measure. Stochastic Processes, Estimation, and Control: The Entropy Approach is the first book to apply the thermodynamic principle of entropy to the measurement and analysis of uncertainty in systems. Its new reformulation takes an important first step toward a unified approach to the theory of intelligent machines, where artificial intelligence and system theory come together.

Stochastic Processes Estimation And Control

Author : JASON L.. CHUNG SPEYER (WALTER H.)
ISBN : 1611971950
Genre :
File Size : 88. 42 MB
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Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities. The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter as well as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to H2 and H-inf controllers and system robustness. Stochastic Processes, Estimation, and Control is divided into three related sections. First, the authors present the concepts of probability theory, random variables, and stochastic processes, which lead to the topics of expectation, conditional expectation, and discrete-time estimation and the Kalman filter. After establishing this foundation, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems, resulting in controllers with significant practical application.

Discrete Time Stochastic Systems

Author : Torsten Söderström
ISBN : 1852336498
Genre : Mathematics
File Size : 83. 70 MB
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This comprehensive introduction to the estimation and control of dynamic stochastic systems provides complete derivations of key results. The second edition includes improved and updated material, and a new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control.

Stochastic Systems

Author : P. R. Kumar
ISBN : 9781611974256
Genre : Mathematics
File Size : 80. 65 MB
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Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.

Stochastic Models Estimation And Control

Author : Peter S. Maybeck
ISBN : 0080960030
Genre : Mathematics
File Size : 51. 17 MB
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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.

Stochastic Models Estimation And Control

Author : Maybeck
ISBN : 9780080956510
Genre : Mathematics
File Size : 45. 3 MB
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Stochastic Models: Estimation and Control: v. 2

Stochastic Processes And Filtering Theory

Author : Andrew H. Jazwinski
ISBN : 9780486318196
Genre : Science
File Size : 76. 40 MB
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This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and stochastic processes, the author introduces and defines the problems of filtering, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear problems. The final chapters deal with applications, addressing the development of approximate nonlinear filters, and presenting a critical analysis of their performance.

Stochastic Optimal Linear Estimation And Control

Author : James S. Meditch
ISBN : UOM:39015006376852
Genre : Science
File Size : 30. 17 MB
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