1 edition of Statistical decision theory in adaptive control systems found in the catalog.
|Statement||Yoshikazu Sawaragi and Yoshifumi Sunahara, and Takayoshi Nakamizo|
|Series||Mathematics in science and engineering -- 39, Mathematics in science and engineering -- v. 39.|
|LC Classifications||QA402.3 .S27 2010eb|
|The Physical Object|
|Format||[electronic resource] /|
|Pagination||1 online resource (xiii, 216 p.)|
|Number of Pages||216|
Adaptive control is an active field in the design of control systems to deal with uncertainties. The key difference between adaptive controllers and linear controllers is the adaptive controller’s ability to adjust itself to handle unknown model uncertainties. Adaptive control is roughly divided into two categories: direct and by: In many applications of control theory, the dynamics of the plant are incompletely known at best. Furthermore, the dynamics are often time varying and non-linear. In such an environment, control becomes a very difficult task and the problem of the optimal control of such systems remains unsolved. However, such systems need to be controlled and so.
Ch. 2: Forecasting and Decision Theory 83 Preface This chapter hastwo sections. Section 1 presentsa fairly brief history of the interaction of forecasting and decision theory,and Section 2 presents some more recent results. 1. History of the ﬁeld Introduction A decision maker (either a private agent or a public policy maker) must. Adaptive Computation and Machine Learning series Adaptive Computation and Machine Learning series The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science.
Applied Statistical Decision Theory HOWARD RAIFFA ROBERT SCHLAIFER Wiley Classics Library Edition Published A Wiley-Interscience Publication JOHN WILEY & SONS, INC. New York • Chichester • Weinheim • Brisbane • Singapore • Toronto. CONTENTS Foreword v. - Lind's supporting pedagogy includes self-reviews, cumulative exercises, and coverage of software applications including Excel, Minitab, and MegaStat for Excel. - Connect: A highly reliable, easy-to-use homework and learning management solution that embeds learning science and award-winning adaptive tools to improve student Edition:
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Mathematics in Science and Engineering, Volume Statistical Decision Theory in Adaptive Control Systems focuses on the combination of control theory with statistical decision theory.
This volume is divided into nine chapters. Chapter 1 reviews the history of control theory and introduces statistical decision Edition: 1. Additional Physical Format: Online version: Sawaragi, Yoshikazu, Statistical decision theory in adaptive control systems.
New York, Academic Press, This book is recommended for students and researchers concerned with statistical decision theory in adaptive control systems. Mathematics in Science and Engineering, Volume Statistical Decision Theory in Adaptive Control Systems focuses on the combination of control theory with statistical decision theory.
Chapter 8 is devoted to the description of a method of the adaptive adjustment of parameters contained in nonlinear control systems, followed by a discussion of the future problems in applications of statistical decision theory to control processes in the last chapter.
Statistical Decision Theory in Adaptive Control Systems by Yoshikazu Sawaragi, Yoshfumi Sunahara and Takayoshi Nakamizo In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. Statistical Decision Theory in Adaptive Control Systems by Yoshikazu Sawaragi, Yoshfumi.
Purchase Statistical Decision Theory in Adaptive Control Systems by Yoshikazu Sawaragi, Yoshfumi Sunahara and Takayoshi Nakamizo, Volume 39 - 1st Edition.
Print Book & E-Book. ISBNBook Edition: 1. Decision theory (or the theory of choice not to be confused with choice theory) is the study of an agent's choices. Decision theory can be broken into two branches: normative decision theory, which analyzes the outcomes of decisions or determines the optimal decisions given constraints and assumptions, and descriptive decision theory, which analyzes how agents actually make.
The basic concept of learning control systems is introduced. Synthesis of learning control systems using statistical decision theory is discussed. Learning is needed if the a priori information is unknovn or incompletely known in an adaptive system.
The controller will establish the necessary information for control during the system’s : K.S. Statistical decision is an effective method to control an unknown system with the aid of incomplete or slight information (e.g., a few past control experiences about the system).
The statistical decision method itself has no learning : Shigeru Eiho, Bunji Kondo. The mathematical prerequisites are small - some calculus, but the book manages to introduce basic probability, stochastic processes, statistical inference, large sample theory, the multivariate normal distribution, and topics related to economics such as utility functions, lotteries, decision trees and portfolio by: Tm- 14 IV Elements of Statistical Decision T e r hoy portant results in this approach were obtained by Pugachev [l Statistical decision theory was applied in the design of adaptive systems in the book by Sawaragi et al.
[l]. REFERENCES Barabash, Yu. L., Varskii, B. V., Zinovyev, V. T.,Kirichenko, V. S., and Sapegin, V. Adaptive control or self-tuning control of multivariable controllers for multivariable processes (MIMO systems); Usually these methods adapt the controllers to both the process statics and dynamics.
In special cases the adaptation can be limited to the static behavior alone, leading to adaptive control based on characteristic curves for the. The method of backward induction as used in solving statistical problems in sequential analysis may be regarded a precursor of the technique of dynamic programming.
The technique has been found useful in solving problems in control theory, sequential decision theory, and the theory of adaptive processes. Starting with a broad overview, the text explores real-time estimation, self-tuning regulators and model-reference adaptive systems, stochastic adaptive control, and automatic tuning of regulators.
Additional topics include gain scheduling, robust high-gain control and self-oscillating controllers, and suggestions for implementing adaptive /5(22). Decision theory as the name would imply is concerned with the process of making decisions.
The extension to statistical decision theory includes decision making in the presence of statistical knowledge which provides some information where there is uncertainty.
The elements of decision theory are quite logical and even perhaps intuitive. "In the field of statistical decision theory, Raiffa and Schlaifer have sought to develop new analytic techniques by which the modern theory of utility and subjective probability can actually be applied to the economic analysis of typical sampling problems." --From the foreword to their classic work "Applied Statistical Decision Theory," First published in the s through Harvard University.
Theory of Self-Adaptive Control Systems A Class of Learning Control Systems Using Statistical Decision Processes. Theory of Self-Adaptive Control Systems Book Subtitle Proceedings of the Second IFAC Symposium on the Theory of Self-Adaptive Control Systems September 14–17, National Physical Laboratory Teddington, England.
(shelved 1 time as decision-theory) avg rating — 21, ratings — published Want to Read saving. Mathematical control theory is the area of application-oriented mathematics that deals with the basic principles underlying the analysis and design of control systems.
Readership: Systems scientists, management scientists, engineers, people working in operations research, cybernetics & control theory and those interested in learning about new directions in decision making and control. The Bayesian revolution in statistics—where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine—is here to stay.
Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under.
Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence.
Statistical Decision Theory an area of mathematical statistics and game theory that permits a number of diverse problems to be treated in a common manner. Such problems include the statistical testing of hypotheses, the obtaining of statistical estimators for parameters and of confidence limits for the estimators, and the design of experiments.