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Deterministic Learning Theory for Identification, Recognition, and Control

By Cong Wang, David J. Hill

Series Editor: Frank L. Lewis

CRC Press – 2009 – 207 pages

Series: Automation and Control Engineering

Purchasing Options:

  • Add to CartHardback: $164.95
    978-0-8493-7553-8
    July 21st 2009

Description

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).

Contents

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

Name: Deterministic Learning Theory for Identification, Recognition, and Control (Hardback)CRC Press 
Description: By Cong Wang, David J. HillSeries Editor: Frank L. Lewis. 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...
Categories: Machine Learning, Systems & Controls, Aerospace Engineering, Intelligent Systems