UID:
almafu_9958071906902883
Umfang:
1 online resource (231 p.)
ISBN:
1-283-52568-2
,
9786613838131
,
0-08-095546-0
Serie:
Mathematics in science and engineering ; 39
Inhalt:
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank
Anmerkung:
Description based upon print version of record.
,
Front Cover; Statistical Decision Theory in Adaptive Control Systems; Copyright Page; Contents; Preface; Chapter 1. Introduction; 1.1 Historical Development of Automatic Control; 1.2 Control Systems and Stochastics; 1.3 Adaptive Control and Decision Theory; Chapter 2. Mathematical Description of Random Processes; 2.1 Introductory Remarks; 2.2 Probability; 2.3 Joint Probability; 2.4 Conditional Probability; 2.5 Bayes' Theorem; 2.6 Probability Distribution and Probability Density Function; 2.7 Joint Probability Distribution and Joint Probability Density Function
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2.8 Conditional Probability Distribution and Conditional Probability Density Function2.9 Statistical Parameters of Random Variables; 2.10 Stochastic Processes; 2.11 Stationary Random Processes; 2.12 Ergodic Hypothesis and Time Averages; 2.13 Stationary Gaussian Random Processes; Chapter 3. Basic Concept of Statistical Decision Theory; 3.1 Introductory Remarks; 3.2 General Description of the Decision Situation; 3.3 Signal Detection; 3.4 Signal Extraction; Chapter 4. Evaluation Functions and Solutions in Statistical Decision Theory; 4.1 Introductory Remarks; 4.2 Basic Assumptions
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4.3 General Formulation of Evaluation Functions in Decision Problems4.4 Solutions of Decision Problems by the Bayes Criterion; 4.5 Solutions of Binary Detection Problems; 4.6 The Neyman-Pearson Detection Rule; Chapter 5. Statistical Decision Concept in Control Processes; 5.1 Introductory Remarks; 5.2 Decision Adaptive Control Systems under Preassigned Error Probabilities; 5.3 Binary Decision Adaptive Control Systems Based on the Concept of the Sequential Test; 5.4 Decision Adaptive Control Systems Based on the Neyman-Pearson Test; 5.5 Ideal-Observer Decision-Making
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Chapter 6. Nonsequential Decision Approaches in Adaptive Control Systems6.1 Introductory Remarks; 6.2 Extension of the Binary Detection Concept to N-ary Decision Problems; 6.3 Derivation of the Bayesian System; 6.4 Construction of a Decision System Subjected to Gaussian Random Noise; 6.5 Decision-Making in System ldentification; 6.6 Decision-Making in System ldentification with Gaussian Random Noise; 6.7 Numerical Examples of Application of Decision Concept to Averaging Devices; 6.8 Application of Decision Concept to Nondata Problems
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Chapter 7. Sequential Decision Approaches in Adaptive Control Systems7.1 Introductory Remarks; 7.2 An Average Risk of Sequential Decision Procedure; 7.3 Derivation of Bayes Solution; 7.4 Application of Sequential Decision-Making to Adaptive Control Systems; 7.5 Operating Characteristic Function (OC Function) and Average Sample Number Function (ASN Function); 7.6 Average Amount of Observation Time; 7.7 Numerical Example; 7.8 Comparison of Sequential and Nonsequential Decision Procedures; Chapter 8. Adaptive Adjustment of Parameters of Nonlinear Control Systems; 8.1 Introductory Remarks
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8.2 Application of Sequential Decision Rule
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English
Weitere Ausg.:
ISBN 0-12-620350-4
Sprache:
Englisch
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