1st Edition
Stability Analysis and State Estimation of Memristive Neural Networks
1 Introduction
1.1 Background on Memristive Neural Networks
1.2 MNNs subject to Engineering-Oriented Complexities
1.3 Design Techniques
1.4 Outline
2 H1 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 H1 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 H1 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 H1 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
7.2 Main Results
7.3 An Illustrative Example
7.4 Summary
8 On Finite-Horizon H1 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 H1 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 H1 and l2-l1 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
Biography
Hongjian Liu is currently a Professor in the School of Mathematics and Physics, Anhui Polytechnic University, Wuhu, China. His current research interests include filtering theory, memristive neural networks and network communication systems. He is a very active reviewer for many international journals.
Zidong Wang is currently Professor of Dynamical Systems and Computing at Brunel University London in the United Kingdom. His research interests include dynamical systems, signal processing, bioinformatics, control theory and applications.
Lifeng Ma is currently a Professor with the School of Automation, Nanjing University of Science and Technology, Nanjing, China. His current research interests include nonlinear control and signal processing, variable structure control, distributed control and filtering, time-varying systems, and multi-agent systems.
