Stability Analysis and State Estimation of Memristive Neural Networks

Hongjian Liu, Zidong Wang, Lifeng Ma

• Publisher: CRC Press
• Year: 2021
• Print ISBN:
  9781032037103
• eBook ISBN:
  9781000415087
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

In this book, the stability analysis and estimator design problems are discussed for delayed discrete-time memristive neural networks. In each chapter, the analysis problems are firstly considered, where the stability, synchronization and other performances (e.g., robustness, disturbances attenuation level) are investigated within a unified theoretical framework. In this stage, some novel notions are put forward to reflect the engineering practice. Then, the estimator design issues are discussed where sufficient conditions are derived to ensure the existence of the desired estimators with guaranteed performances. Finally, the theories and techniques developed in previous parts are applied to deal with some issues in several emerging research areas.

The book

• Unifies existing and emerging concepts concerning delayed discrete memristive neural networks with an emphasis on a variety of network-induced phenomena
• Captures recent advances of theories, techniques, and applications of delayed discrete memristive neural networks from a network-oriented perspective
• Provides a series of latest results in two popular yet interrelated areas, stability analysis and state estimation of neural networks
• Exploits a unified framework for analysis and synthesis by designing new tools and techniques in combination with conventional theories of systems science, control engineering and signal processing
• Gives simulation examples in each chapter to reflect the engineering practice

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Acknowledgment
• Authors Biographies
• List of Figures
• List of Tables
• Symbols
• 1 Introduction
• 2 H∞ State Estimation for Discrete-Time Memristive Recurrent Neural Networks with Stochastic Time-Delays
• 3 Event-Triggered H∞ State Estimation for Delayed Stochastic Memristive Neural Networks with Missing Measurements: The Discrete Time Case
• 4 H∞ State Estimation for Discrete-Time Stochastic Memristive BAM Neural Networks with Mixed Time-Delays
• 5 Stability Analysis for Discrete-Time Stochastic Memristive Neural Networks with Both Leakage and Probabilistic Delays
• 6 Delay-Distribution-Dependent H∞ State Estimation for Discrete-Time Memristive Neural Networks with Mixed Time-Delays and Fading Measurements
• 7 On State Estimation for Discrete Time-Delayed Memristive Neural Networks under the WTOD Protocol: A Resilient Set-Membership Approach
• 8 On Finite-Horizon H∞ State Estimation for Discrete-Time Delayed Memristive Neural Networks under Stochastic Communication Protocol
• 9 Resilient H∞ State Estimation for Discrete-Time Stochastic Delayed Memristive Neural Networks: A Dynamic Event-Triggered Mechanism
• 10 H∞ and l2−l∞ State Estimation for Delayed Memristive Neural Networks on Finite Horizon: The Round-Robin Protocol
• 11 Conclusions and Future Topics
• Bibliography
• Index