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.
Audience: 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.
Contents: 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
"synopsis" may belong to another edition of this title.
A succinct and rigorous treatment of the foundations of stochastic control, ideal for advanced students already acquainted with stochastic processes. This book presents a unified approach to filtering, estimation, prediction, and stochastic and adaptive control, while also providing the conceptual framework necessary to understand current trends.About the Author:
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).
"About this title" may belong to another edition of this title.
Book Description Prentice Hall, 1986. Hardcover. Book Condition: New. Never used!. Bookseller Inventory # P11013846684X
Book Description Prentice Hall, 1986. Hardcover. Book Condition: New. book. Bookseller Inventory # M013846684X