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Deterministic Learning Theory for Identification, Recognition, and Control: For Identiflcation, Recognition, and Conirol [Hardback]

(Australian National University Research, Act, Australia), (School of Automation, South China University of Technology, Guangzhou, China)
  • Formāts: Hardback, 207 pages, height x width: 234x156 mm, weight: 498 g, 147 Illustrations, black and white
  • Sērija : Automation and Control Engineering
  • Izdošanas datums: 21-Jul-2009
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 0849375533
  • ISBN-13: 9780849375538
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Deterministic Learning Theory for Identification, Recognition, and Control: For Identiflcation, Recognition, and ConirolPalielināt
  • Formāts: Hardback, 207 pages, height x width: 234x156 mm, weight: 498 g, 147 Illustrations, black and white
  • Sērija : Automation and Control Engineering
  • Izdošanas datums: 21-Jul-2009
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 0849375533
  • ISBN-13: 9780849375538
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780849375538.html
Keywords:
Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.

A Deterministic View of Learning in Dynamic Environments

The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.

A New Model of Information Processing

This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).
Preface xi
Acknowledgments xv
About the Authors xvii
1 Introduction 1
1.1 Learning Issues in Feedback Control
1
1.1.1 Adaptive and Learning Control
1
1.1.2 Intelligent Control and Neural Network Control
4
1.2 Learning Issues in Temporal Pattern Recognition
6
1.2.1 Pattern Recognition in Feedback Control
6
1.2.2 Representation, Similarity, and Rapid Recognition
7
1.3 Preview of the Main Topics
9
1.3.1 RBF Networks and the PE Condition
9
1.3.2 The Deterministic Learning Mechanism
10
1.3.3 Learning from Adaptive Neural Network Control
11
1.3.4 Dynamical Pattern Recognition
12
1.3.5 Pattern-Based Learning Control
13
1.3.6 Deterministic Learning Using Output Measurements
14
1.3.7 Nature of Deterministic Learning
15
2 RBF Network Approximation and Persistence of Excitation 17
2.1 RBF Approximation and RBF Networks
18
2.1.1 RBF Approximation
18
2.1.2 RBF Networks
20
2.2 Persistence of Excitation and Exponential Stability
23
2.3 PE Property for RBF Networks
27
3 The Deterministic Learning Mechanism 37
3.1 Problem Formulation
38
3.2 Locally Accurate Identification of Systems Dynamics
39
3.2.1 Identification with σ-Modification
40
3.2.2 Identification without Robustification
44
3.3 Comparison with System Identification
46
3.4 Numerical Experiments
49
3.5 Summary
58
4 Deterministic Learning from Closed-Loop Control 61
4.1 Introduction
61
4.2 Learning from Adaptive NN Control
62
4.2.1 Problem Formulation
62
4.2.2 Learning from Closed-Loop Control
63
4.2.3 Simulation Studies
70
4.3 Learning from Direct Adaptive NN Control of Strict-Feedback Systems
75
4.3.1 Problem Formulation
76
4.3.2 Direct ANC Design
77
4.3.3 Learning from Direct ANC
79
4.4 Learning from Direct ANC of Nonlinear Systems in Brunovsky Form
82
4.4.1 Stability of a Class of Linear Time-Varying Systems
83
4.4.2 Learning from Direct ANC
86
4.4.3 Simulation Studies
92
4.5 Summary
95
5 Dynamical Pattern Recognition 97
5.1 Introduction
97
5.2 Time-Invariant Representation
99
5.2.1 Static Representation
99
5.2.2 Dynamic Representation
100
5.2.3 Simulations
101
5.3 A Fundamental Similarity Measure
104
5.4 Rapid Recognition of Dynamical Patterns
107
5.4.1 Problem Formulation
108
5.4.2 Rapid Recognition via Synchronization
109
5.4.3 Simulations
112
5.5 Dynamical Pattern Classification
117
5.5.1 Nearest-Neighbor Decision
117
5.5.2 Qualitative Analysis of Dynamical Patterns
118
5.5.3 A Hierarchical Structure
119
5.6 Summary
121
6 Pattern-Based Intelligent Control 123
6.1 Introduction
123
6.2 Pattern-Based Control
124
6.2.1 Definitions and Problem Formulation
124
6.2.2 Control Based on Reference Dynamical Patterns
126
6.2.3 Control Based on Closed-Loop Dynamical Patterns
127
6.3 Learning Control Using Experiences
128
6.3.1 Problem Formulation
128
6.3.2 Neural Network Learning Control
129
6.3.3 Improved Control Performance
132
6.4 Simulation Studies
133
6.5 Summary
137
7 Deterministic Learning with Output Measurements 139
7.1 Introduction
139
7.2 Learning from State Observation
141
7.3 Non-High-Gain Observer Design
146
7.4 Rapid Recognition of Single-Variable Dynamical Patterns
149
7.4.1 Representation Using Estimated States
149
7.4.2 Similarity Definition
151
7.4.3 Rapid Recognition via Non-High-Gain State Observation
152
7.5 Simulation Studies
156
7.6 Summary
165
8 Toward Human-Like Learning and Control 167
8.1 Knowledge Acquisition
167
8.2 Representation and Similarity
169
8.3 Knowledge Utilisation
169
8.4 Toward Human-Like Learning and Control
170
8.5 Cognition and Computation
171
8.6 Comparison with Statistical Learning
172
8.7 Applications of the Deterministic Learning Theory
172
References 175
Index 189
National Natural Science Foundation of China, Haidian, Beiji The University of Texas at Arlington, USA