Deterministic Learning Theory for

Deterministic Learning Theory for Identification, Recognition, and Controlpresents 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).

Introduction
Learning Issues in Feedback Control
Learning Issues in Temporal Pattern Recognition
Preview of the Main Topics
RBF Network Approximation and Persistence of Excitation
RBF Approximation and RBF Networks
Persistence of Excitation and Exponential Stability
PE Property for RBF Networks
The Dgeterministic Learning Mechanism
Problem Formulation
Locally-Accurate Identification of Systems Dynamics
Comparison with System Identification
Numerical Experiments
Summary
Deterministic Learning From Closed-Loop Control
Introduction
Learning from Adaptive NN Control
Learning from Direct Adaptive NN Control of Strict-Feedback Systems
Learning From Direct Adaptive NN Control of Nonlinear Systems in Brunovsky Form
Summary
Dynamical Pattern Recognition
Introduction
Time-Invariant Representation
A Fundamental Similarity Measure
Rapid Recognition of Dynamical Patterns
Dynamical Pattern Classification
Summary
Pattern-Based Learning Control
Introduction
Pattern-Based Control
Learning Control Using Experiences
Simulation Studies
Summary
Deterministic Learning with Output Measurements
Introduction
Learning from State Observation
Non-High-Gain Observer Design
Rapid Recognition of Single-Variable Dynamical Patterns
Simulation Studies
Summary
Toward Human-Like Learning and Control
Knowledge Acquisition
Representation and Similarity
Knowledge Utilization
Toward Human-Like Learning and Control
Cognition and Computation
Comparison with Statistical Learning
Applications of the Deterministic Learning Theory
References
Index
Table of Contents provided by Publisher. All Rights Reserved.

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Deterministic Learning Theory for Identification, Recognition, and Control

Summary

Table of Contents

Supplemental Materials