Stochastic Systems
Estimation, Identification, and Adaptive Control
Will be published approx. on 30. March 2015
Book
Paperback/Softback
375 pages
978-1-61197-425-6 (ISBN)
Description
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 cools
The conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
This book provides:
Succinct and rigorous treatment of the foundations of stochastic control.
A unified approach to filtering, estimation, prediction, and stochastic and adaptive cools
The conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
More details
Series
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 227 mm
Width: 152 mm
Thickness: 19 mm
Weight
522 gr
ISBN-13
978-1-61197-425-6 (9781611974256)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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).
Content
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
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