1st Edition
Deterministic Learning Theory for Identification, Recognition, and Control
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
Biography
Cong Wang, David J. Hill
