Pravin Varaiya
人物简介:
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).
Stochastic Systems: Estimation, Identification, and Adaptive Control书籍相关信息
- ISBN:9781611974256
- 作者:P. R. Kumar / Pravin Varaiya
- 出版社:SIAM-Society for Industrial and Applied Mathematics
- 出版时间:2016-10-17
- 页数:378
- 价格:USD 81.50
- 纸张:暂无纸张
- 装帧:Paperback
- 开本:暂无开本
- 语言:暂无语言
- 丛书:Classics in Applied Mathematics
- 适合人群:Engineers, Researchers, Postgraduate Students, Professors in fields of Control Systems, Electrical Engineering, Mechanical Engineering, and related disciplines interested in the mathematical modeling and analysis of stochastic systems
- TAG:Probability Theory / Engineering Mathematics / Stochastic Processes / Control Theory / System Identification / Adaptive Control
- 豆瓣评分:暂无豆瓣评分
- 更新时间:2025-05-07 15:05:14
内容简介:
Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with applications in several branches of engineering and in areas of the social sciences concerned with policy analysis and prescription. With the increase in computational capacity and the ability to collect and process huge quantities of data, an explosion of work in the area has been engendered. 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, learning, and robotics. It is ideal for students previously acquainted with probability theory and stochastic processes, who wish to learn more on decision making with uncertainty, and can be used as a course textbook for advanced undergraduate or first year graduate students.