內容大鋼
《量化系統的時間驅動辨識（英文版）》由郭金、刁靖東編著。
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.
目錄
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
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