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

ISBN-13: 9781138112056 / Angielski / Miękka / 2017 / 207 str.

Cong Wang; David J. Hill

Opis

Offering a new perspective, this book provides systematic design approaches for the identification, control, and recognition of nonlinear systems in uncertain environments. It introduces the concepts of deterministic learning theory and then discusses the persistent excitation property of RBF networks. The authors describe the theory of deterministic learning processes and address dynamical pattern recognition and pattern-based control processes. They present a new model of dynamical parallel distributed processing applicable to the detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.

Spis treści

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

O autorze

Cong Wang, David J. Hill