| Description |
1 online resource (xix, 291 pages) : illustrations |
| Series |
Mathematics in science and engineering ; v. 73 |
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Mathematics in science and engineering ; v. 73.
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| Contents |
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 |
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2.2 Conditions of Optimality2.3 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 |
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CommentsBibliography; 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 |
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3.17 The Best Algorithms3.18 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 |
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4.9 Continuous Algorithms of Training4.10 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 |
| Summary |
Adaptation and learning in automatic systems |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL |
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Print version record |
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digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL |
| Subject |
Adaptive control systems.
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Self-organizing systems.
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TECHNOLOGY & ENGINEERING -- Automation.
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TECHNOLOGY & ENGINEERING -- Robotics.
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Adaptive control systems
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Self-organizing systems
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| Form |
Electronic book
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| ISBN |
9780127020501 |
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0127020500 |
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9780080955827 |
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0080955827 |
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1282288598 |
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9781282288591 |
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