1st Edition

Deterministic Learning Theory for Identification, Recognition, and Control

By Cong Wang, David J. Hill Copyright 2010
    207 Pages 147 B/W Illustrations
    by CRC Press

    207 Pages 147 B/W Illustrations
    by CRC Press

    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).


    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


    Deterministic Learning From Closed-Loop Control


    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


    Dynamical Pattern Recognition


    Time-Invariant Representation

    A Fundamental Similarity Measure

    Rapid Recognition of Dynamical Patterns

    Dynamical Pattern Classification


    Pattern-Based Learning Control


    Pattern-Based Control

    Learning Control Using Experiences

    Simulation Studies


    Deterministic Learning with Output Measurements


    Learning from State Observation

    Non-High-Gain Observer Design

    Rapid Recognition of Single-Variable Dynamical Patterns

    Simulation Studies


    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




    Cong Wang, David J. Hill