Stability Analysis and State Estimation of Memristive Neural Networks

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgment
Authors Biographies
List of Figures
List of Tables
Symbols
1. Introduction
	1.1. Background on Memristive Neural Networks
		1.1.1. Memristor and Its Circuit Realization
		1.1.2. Stability Analysis and State Estimation for MNNs
		1.1.3. Recent Progress on Several Types of Neural Networks
			1.1.3.1. RNNs
			1.1.3.2. BAMNNs
			1.1.3.3. CMNNs
	1.2. MNNs subject to Engineering-Oriented Complexities
		1.2.1. Stochasticity
		1.2.2. Time-Delays
		1.2.3. Network-Induced Incomplete Information
			1.2.3.1. Missing Measurements
			1.2.3.2. Channel Fading
			1.2.3.3. Signal Quantization
	1.3. Design Techniques
		1.3.1. Event-Triggering Mechanisms
		1.3.2. Network Communication Protocols
			1.3.2.1. RR Protocol
			1.3.2.2. WTOD Protocol
			1.3.2.3. SC Protocol
		1.3.3. Set-Membership Technique
		1.3.4. Non-Fragile Algorithm
	1.4. Outline
2. H∞ State Estimation for Discrete-Time Memristive Recurrent Neural Networks with Stochastic Time-Delays
	2.1. Problem Formulation
	2.2. Main Results
	2.3. An Illustrative Example
	2.4. Summary
3. Event-Triggered H∞ State Estimation for Delayed Stochastic Memristive Neural Networks with Missing Measurements: The Discrete Time Case
	3.1. Problem Formulation
	3.2. Main Results
	3.3. An Illustrative Example
	3.4. Summary
4. H∞ State Estimation for Discrete-Time Stochastic Memristive BAM Neural Networks with Mixed Time-Delays
	4.1. Problem Formulation and Preliminaries
	4.2. Main Results
	4.3. Numerical Example
	4.4. Summary
5. Stability Analysis for Discrete-Time Stochastic Memristive Neural Networks with Both Leakage and Probabilistic Delays
	5.1. Problem Formulation
	5.2. Main Results
	5.3. Illustrative Examples
	5.4. Summary
6. Delay-Distribution-Dependent H∞ State Estimation for Discrete-Time Memristive Neural Networks with Mixed Time-Delays and Fading Measurements
	6.1. Problem Formulation
	6.2. Main Results
	6.3. Illustrative Examples
	6.4. Summary
7. On State Estimation for Discrete Time-Delayed Memristive Neural Networks under the WTOD Protocol: A Resilient Set-Membership Approach
	7.1. Problem Formulation
		7.1.1. Memristive Neural Network Model
		7.1.2. The WTOD Protocol
	7.2. Main Results
	7.3. An Illustrative Example
	7.4. Summary
8. On Finite-Horizon H∞ State Estimation for Discrete-Time Delayed Memristive Neural Networks under Stochastic Communication Protocol
	8.1. Problem Formulation and Preliminaries
	8.2. Main Results
	8.3. An Illustrative Example
	8.4. Summary
9. Resilient H∞ State Estimation for Discrete-Time Stochastic Delayed Memristive Neural Networks: A Dynamic Event-Triggered Mechanism
	9.1. Problem Formulation
	9.2. Main Results
	9.3. An Illustrative Example
	9.4. Summary
10. H∞ and l2 - l∞ State Estimation for Delayed Memristive Neural Networks on Finite Horizon: The Round-Robin Protocol
	10.1. Problem Formulation and Preliminaries
	10.2. Main Results
	10.3. An Illustrative Example
	10.4. Summary
11. Conclusions and Future Topics
Bibliography
Index