Event-Triggered Identification of Systems with Quantized Observations

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