2015 / xviii + 358 pages / Softcover / ISBN 978-1-611974-25-6 / List Price $74.00 / SIAM Member Price $51.80 / Order Code CL75
Keywords: stochastic systems, estimation, adaptive control, Markov decision process, dynamic programming
Preface to the Classics Edition;
Chapter 1: Introduction;
Chapter 2: State space models;
Chapter 3: Properties of linear stochastic systems;
Chapter 4: Controlled Markov chain model;
Chapter 5: Input output models;
Chapter 6: Dynamic programming;
Chapter 7: Linear systems: estimation and control;
Chapter 8: Infinite horizon dynamic programming;
Chapter 9: Introduction to system identification;
Chapter 10: Linear system identification;
Chapter 11: Bayesian adaptive control;
Chapter 12: Non-Bayesian adaptive control;
Chapter 13: Self-tuning regulators for linear systems;
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
This book is recommended for those who have been introduced to probability theory and stochastic processes and want to learn more about decision making under uncertainty. It can be used as a one- or two-semester course textbook for advanced undergrad or first-year graduate students.
About the Authors
P. R. Kumar is a University Distinguished Professor and holds the College of Engineering Chair in Computer Engineering at Texas A&M University. His current research focuses on renewable energy systems, wireless networks, secure systems, automated transportation, and cyberphysical systems. He is a Fellow of the World Academy of Sciences, ACM, and IEEE, and a member of the U.S. National Academy of Engineering. He serves as an editor-at-large of IEEE/ACM Transactions on Networking, and has co-authored three other books, most recently, A Clean Slate Approach to Secure Wireless Networking, NOW (2015).
Pravin Varaiya is a Professor of the Graduate School in the Department of Electrical Engineering and Computer Sciences at 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, Birkhäuser (2014).
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