Stochastic Systems

Estimation, Identification, and Adaptive Control

Häftad, Engelska, 2015

Av P.R. Kumar, Pravin Varaiya, P. R. Kumar, P R Kumar

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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 coolsThe conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.

Produktinformation

Utgivningsdatum2015-03-30
Mått152 x 229 x 19 mm
Vikt516 g
FormatHäftad
SpråkEngelska
SerieClassics in Applied Mathematics
Antal sidor375
FörlagSociety for Industrial & Applied Mathematics,U.S.
ISBN9781611974256

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P. R. Kumar is currently a University Distinguished Professor and holds the College of Engineering Chair in Computer Engineering at Texas A&M University. His research is focused on energy systems, wireless networks, secure networking, automated transportation, and cyberphysical systems. Kumar is a member of the US National Academy of Engineering and a Fellow of the World Academy of Sciences, ACM, and IEEE. Pravin Varaiya is a Professor of the Graduate School in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His current research focuses on transportation networks and electric power systems. He is a Fellow of IEEE and the American Academy of Arts and Sciences, and a member of the US National Academy of Engineering. He is on the editorial board of Transportation Letters and has co-authored four books, most recently, Dynamics and Control of Trajectory Tubes (2014).

Chapter 1: IntroductionChapter 2: State space modelsChapter 3: Properties of linear stochastic systemsChapter 4: Controlled Markov chain modelChapter 5: Input output modelsChapter 6: Dynamic programmingChapter 7: Linear systems: estimation and controlChapter 8: Infinite horizon dynamic programmingChapter 9: Introduction to system identificationChapter 10: Linear system identificationChapter 11: Bayesian adaptive controlChapter 12: Non-Bayesian adaptive controlChapter 13: Self-tuning regulators for linear systems