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Stochastic Systems

Stochastic Systems
Estimation, Identification, and Adaptive Control

£53.99

Part of Classics in Applied Mathematics

  • Date Published: February 2016
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial null Mathematics for availability.
  • format: Paperback
  • isbn: 9781611974256

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About the Authors
  • 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.

    • Formulates a unified mathematical framework to address questions of modelling system evolution
    • Provides the conceptual framework necessary to understand current trends in stochastic control, data mining, learning, and robotics
    • Can be used as a course textbook for advanced undergraduate or first year graduate students
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    Product details

    • Date Published: February 2016
    • format: Paperback
    • isbn: 9781611974256
    • length: 378 pages
    • dimensions: 227 x 152 x 19 mm
    • weight: 0.52kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial null Mathematics for availability.
  • Table of Contents

    Preface to the classics edition
    Preface
    1. Introduction
    2. State space models
    3. Properties on linear stochastic systems
    4. Controlled Markov chain model
    5. Input output models
    6. Dynamic programming
    7. Linear systems: estimation and control
    8. Infinite horizon dynamic programming
    9. Introduction to system identification
    10. Linear system identification
    11. Bayesian adaptive control
    12. Non-Bayesian adaptive control
    13. Self-tuning regulators for linear systems
    References
    Author index
    Subject index.

  • Authors

    P. R. Kumar, Texas A & M University
    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, University of California, Berkeley
    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).

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