导语

  

内容提要

  

    《量化系统的时间驱动辨识（英文版）》由郭金、刁靖东编著。
    There are 8 chapters in this book. Chapter 1 introduces the system identification problem with quantized and event-triggered observations. Chapter 2 gives a brief review onseveral optimization and identifcaion techniques. Chapters 3-6 focus on tile liuear system identifaicon under the send-on-delta mechanism (predetermined and adaptive), theprediction-based communication scheme and the either-or communication scheme.Chapters 7- 8 turn the attention to two kinds of basic block-oriented nonlinear sys-tems-Wiener systems and Hammerstein systems.
    The book is written for researchers and engineers working in systems and control,communication and computer networks, etc.

1  Introduction
  1.1  Motivation
  1.2  Event-Triggered Identification with Quantized Observations
  1.3  Outline of the Book
2  Preliminaries
  2.1  Least Square
  2.2  Stochastic Approximation
  2.3  Empirical-Measure-Based Identification
  2.4  Quantized Input-Output Identification
  2.5  Notes
3  FIR System Identification with Scheduled Binary-Valued Observations
  3.1  Problem Formulation
  3.2  Identification Algorithm
  3.3  Convergence Performance
    3.3.1  Convergence and Convergence Rate
    3.3.2  Asymptotically Efficiency
    3.3.3  Communication Rate
  3.4  Numerical Simulation
  3.5  Notes
4  Event-Triggered Identification of FIR Systems with Binary-Valued Observations
  4.1  Problem Formulation
  4.2  Identification Algorithm
  4.3  Properties of the Identification Algorithm
    4.3.1  Strong Convergence
    4.3.2  Convergence Rate
    4.3.3  Implementation of the Event-Triggered Mechanism
    4.3.4  Communication Rate
  4.4  Numerical Simulation
  4.5  Notes
5  Prediction-Based Identification of Quantized-Input FIR Systems with Quantized Observations
  5.1  Problem Formulation
  5.2  Identification Algorithm and Convergence Performance
  5.3  Tradeoff Between the Estimation Performance and the Communication Cost
  5.4  Multi-Threshold Quantized Observations
  5.5  Numerical Simulation
  5.6  Notes
6  FIR System Identification under Either-or Communication with Quantized Inputs and Quantized Observations
  6.1  Problem Formulation
  6.2  Either-or Communication Scheme and Identification Algorithm
  6.3  Convergence Performance
  6.4  Multi-Threshold Quantized Observations
  6.5  Numerical Simulation
  6.6  Notes
7  Event-Triggered Identification of Wiener Systems with Binary-Valued Observations
  7.1  Problem Formulation
  7.2  System Identifiability
  7.3  Identification Algorithm
  7.4  Convergence Properties
    7.4.1  Strong Convergence
    7.4.2  Asymptotic Efficiency
  7.5  Simulation Example
  7.6  Notes
8  Event-Triggered Identification of Hammerstein Systems with Quantized Observations
  8.1  Problem Formulation
  8.2  System IdentifiabUity and Identification Algorithms
  8.3  Convergence Properties
  8.4  Simulation
  8.5  Notes
Appendix A: Mathematical Background
  A.1  Probability Theory
    A.1.1  Some Concepts
    A.l.2  Conditional Expectation and Martingale Difference Sequence
    A.1.3  Cramer-Rao Lower Bound
  A.2  Vector and Matrix
    A.2.1   Vector Norm
    A.2.2  Matrix Norm
    A.2.3  Moore-Penrose Inverse
References