Adaptation and Learning in Automatic Systems

Name: Adaptation and Learning in Automatic Systems
Brand: Academic Press
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Tsypkin(Autor*in)

Tsypkin(Herausgeber*in)

Academic Press

Erschienen am 14. Mai 2014

317 Seiten

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Front Cover
Adaptation and Learning in Automatic Systems
Copyright Page
Contents
Foreword
Preface to the English Edition
Preface to the Russian Edition
Introduction
Chapter 1. Problem of Optimality
1.1 Introduction
1.2 Criteria of Optimality
1.3 More about the Criteria of Optimality
1.4 Constraints
1.5 A Priori and Current Information
1.6 Deterministic and Stochastic Processes
1.7 The Ordinary and Adaptive Approaches
1.8 On Methods for Solving Optimization Problems
1.9 Conclusion
Comments
Bibliography
Chapter 2. Algorithmic Methods of Optimization
2.1 Introduction
2.2 Conditions of Optimality
2.3 Regular Iterative Method
2.4 Algorithms of Optimization
2.5 A Possible Generalization
2.6 Various Algorithms of Optimization
2.7 Search Algorithms of Optimization
2.8 Constraints I
2.9 Constraints II
2.10 The Method of Feasible Directions
2.11 Discussion
2.12 Multistage Algorithms of Optimization
2.13 Continuous Algorithms of Optimization
2.14 Methods of Random Search
2.15 Convergence and Stability
2.16 The Conditions of Convergence
2.17 On Acceleration of Convergence
2.18 On Best Algorithms
2.19 Examples
2.20 Certain Problems
2.21 Conclusion
Comments
Bibliography
Chapter 3. Adaptation and Learning
3.1 Introduction
3.2 Concepts of Learning, Self-Learning and Adaptation
3.3 Formulation of the Problem
3.4 Probabilistic Iterative Methods
3.5 Algorithms of Adaptation
3.6 Search Algorithms of Adaptation
3.7 Constraints I
3.8 Constraints II
3.9 A Generalization
3.10 Multistage Algorithms of Adaptation
3.11 Continuous Algorithms
3.12 Probabilistic Convergence and Stability
3.13 Conditions of Convergence
3.14 Stopping Rules
3.15 Acceleration of Convergence
3.16 Measure of Quality for the Algorithms
3.17 The Best Algorithms
3.18 Simplified Best Algorithms
3.19 A Special Case
3.20 Relationship to the Least-Square Method
3.21 Relationship to the Bayesian Approach
3.22 Relationship to the Maximum Likelihood Method
3.23 Discussion
3.24 Certain Problems
3.25 Conclusion
Comments
Bibliography
Chapter 4. Pattern Recognition
4.1 Introduction
4.2 Discussion of the Pattern Recognition Problem
4.3 Formulation of the Problem
4.4 General Algorithms of Training
4.5 Convergence of the Algorithms
4.6 Perceptrons
4.7 Discrete Algorithms of Training
4.8 Search Algorithms of Training
4.9 Continuous Algorithms of Training
4.10 Comments
4.11 More about Another General Algorithm of Training
4.12 Special Cases
4.13 Discussion
4.14 Self-Learning
4.15 The Restoration of Probability Density Functions and Moments
4.16 Algorithms of Restoration
4.17 Principle of Self-Learning
4.18 Average Risk
4.19 Variation of the Average Risk
4.20 The Conditions for the Minimum of the Average Risk
4.21 Algorithms of Self-Learning
4.22 A Generalization
4.23 Specific Algorithms
4.24 Search Algorithms of Self-Learning
4.25 Discussion
4.26 Certain Problems
4.27 Conclusion
Comments
Bibliography
Chapter 5. Identification
5.1 Introduction
5.2 Estimation of the Mean Value
5.3 Another Approach
5.4 Estimation of the Variance
5.5 Discussion
5.6 Estimation of Correlation Functions
5.7 Estimation of the Characteristics of Nonlinear Elements
5.8 Estimation of the Coefficient of Statistical Linearization
5.9 Special Cases
5.10 Description of Dynamic Plants
5.11 Identification of Nonlinear Plants I
5.12 Identification of Nonlinear Plants II
5.13 Identification of Nonlinear Plants III
5.14 A Special Case
5.15 A Remark
5.16 Identification of Linear Plants I
5.17 Identification of Linear Plants II
5.18 Estimation of Parameters in Systems with Distributed Parameters
5.19 Noise
5.20 Elimination of Noise
5.21 Certain Problems
5.22 Conclusion
Comments
Bibliography
Chapter 6. Filtering
6.1 Introduction
6.2 Criterion of Optimality
6.3 Adaptive Filter
6.4 Special Cases
6.5 Adaptive Equalizer
6.6 Adaptive Filters Based upon Search
6.7 Adaptive Filter Predictor
6.8 Kolmogorov-Wiener Filters
6.9 Statistical Theory of Signal Reception
6.10 Criterion of Optimality in Signal Reception
6.11 A Decision Rule
6.12 Signal Detection in Background Noise I
6.13 Signal Detection in Background Noise II
6.14 Signal Extraction from Background Noise
6.15 Criterion of Optimal Signal Extraction
6.16 Algorithm of Signal Extraction
6.17 More about Signal Extraction from Background Noise
6.18 Another Criterion of Optimality
6.19 Optimal Receiver
6.20 Possible Simplifications
6.21 Recovery of Input Signals
6.22 Algorithm of Signal Recovery
6.23 The Influence of Noise
6.24 Certain Problems
6.25 Conclusion
Comments
Bibliography
Chapter 7. Control
7.1 Introduction
7.2 When Is Adaptation Necessary?
7.3 Statement of the Problem
7.4 Dual Control
7.5 Algorithms of Dual Control
7.6 Adaptive Control Systems I
7.7 Adaptive Control Systems II
7.8 Sensitivity Model
7.9 Adaptive Control Systems III
7.10 Simplified Adaptive Systems
7.11 Systems of Control by Perturbation
7.12 Algorithms of Optimal Control
7.13 Another Possibility
7.14 Extremal Control Systems
7.15 Algorithms of Extremal Control
7.16 Algorithms of Learning
7.17 Continuous Algorithms
7.18 Structural Scheme
7.19 Possible Simplifications
7.20 The Synthesis of Optimal Systems
7.21 Application of Adaptation Algorithms
7.22 The Synthesis of Optimal Systems in the Presence of Noise
7.23 Control and Pattern Recognition
7.24 Generalization of the Methods of Synthesis
7.25 Certain Problems
7.26 Conclusion
Comments
Bibliography
Chapter 8. Reliability
8.1 Introduction
8.2 Concept of Reliability
8.3 Reliability Indices
8.4 Definition of Reliability Indices
8.5 Minimization of Operating Expenditures
8.6 A Special Case
8.7 Minimization of Cost, Weight and Volume
8.8 Algorithms of Minimization
8.9 A Special Case
8.10 Algorithms
8.11 Increase of Reliability by Reserve Components
8.12 Increase of Reliability by Redundant Components
8.13 Design of Complex Systems
8.14 Algorithms of Optimal Productivity
8.15 The min-max Criterion of Optimization
8.16 More about the Design of Complex Systems
8.17 Comment
8.18 Certain Problems
8.19 Conclusion
Comments
Bibliography
Chapter 9. Operations Research
9.1 Introduction
9.2 Planning of Reserves
9.3 The Criterion of Optimality in Planning
9.4 Algorithm of Optimal Planning
9.5 More about the Planning of Reserves
9.6 Optimal Supply
9.7 Optimal Reserves
9.8 A Comment
9.9 Distribution of Production Resources
9.10 An Example
9.11 Distribution of Detection Facilities
9.12 Algorithm for Optimal Distribution
9.13 Distribution of Quantization Regions
9.14 Criterion of Optimal Distribution
9.15 Algorithms of Optimal Evaluation
9.16 Certain Problems
9.17 Conclusion
Comments
Bibliography
Chapter 10. Games and Automata
10.1 Introduction
10.2 The Concept of Games
10.3 The min-max Theorem
10.4 Equations of Optimal Strategies
10.5 Algorithms of Learning the Solution of Games
10.6 Games and Linear Programming
10.7 Control as a Game
10.8 Algorithms of Control
10.9 A Generalization
10.10 Threshold Elements
10.11 Logical Functions and Their Realizations on a Single Threshold Element
10.12 Criterion of Realizability
10.13 Algorithm of Realizability
10.14 Rosenblatt's Perceptron
10.15 Widrow's Adaline
10.16 Training of a Threshold Element
10.17 Automata
10.18 Description of Finite Automata
10.19 Stochastic Finite Automata
10.20 Automaton-Environment Interaction
10.21 The Expediency of Behavior
10.22 Training of Automata
10.23 About Markov Chains
10.24 Markov Learning
10.25 Games of Automata
10.26 Certain Problems
10.27 Conclusion
Comments
Bibliography
Epilogue
Subject Index
Mathematics in Science and Engineering

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