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Advances in Design and Control Ser.: Stochastic Processes, Estimation and Control by W. Chung and J. Speyer (2011, Trade Paperback)
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- Sofort-KaufenStochastic Processes, Estimation, and Control (Advances in Design and Control, S
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Über dieses Produkt
Product Identifiers
PublisherSociety for Industrial AND Applied Mathematics
ISBN-100898716551
ISBN-139780898716559
eBay Product ID (ePID)84309034
Product Key Features
Number of Pages398 Pages
Publication NameStochastic Processes, Estimation and Control
LanguageEnglish
Publication Year2011
SubjectProbability & Statistics / Stochastic Processes, Mechanics / Dynamics, General
TypeTextbook
Subject AreaMathematics, Science
AuthorW. Chung, J. Speyer
SeriesAdvances in Design and Control Ser.
FormatTrade Paperback
Dimensions
Item Height0.6 in
Item Weight24.7 Oz
Item Length9 in
Item Width6 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2008-022483
Dewey Edition22
IllustratedYes
Dewey Decimal519.2/3
Table Of ContentPreface Chapter 1:Probability Theory Chapter 2: Random Variables and Stochastic Processes Chapter 3: Conditional Expectations and Discrete-Time Kalman Filtering Chapter 4: Least Squares, the Orthogonal Projection Lemma, and Discrete-Time Kalman Filtering Chapter 5: Stochastic Processes and Stochastic Calculus Chapter 6: Continuous-Time Gauss--Markov Systems: Continuous-Time Kalman Filter, Stationarity, Power Spectral Density, and the Wiener Filter Chapter 7: The Extended Kalman Filter Chapter 8: A Selection of Results from Estimation Theory Chapter 9: Stochastic Control and the Linear Quadratic Gaussian Control Problem Chapter 10: Linear Exponential Gaussian Control and Estimation Bibliography Index
SynopsisCovers 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., 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.
LC Classification NumberQA274 .S655 2008
