Stability Analysis and State Estimation of Memristive Neural Networks

ISBN-13: 9781032037103 / Angielski / Twarda / 2021 / 214 str.

Hongjian Liu; Zidong Wang; Lifeng Ma

Opis

This book discusses the stability analysis and estimator design problems for discrete-time memristive neural networks subject to time-delays and approaches state estimation from different perspectives. Each chapter includes analysis problems and application of theories and techniques to pertinent research areas.

Spis treści

1 Introduction1.1 Background on Memristive Neural Networks1.2 MNNs subject to Engineering-Oriented Complexities1.3 Design Techniques1.4 Outline 2 H1 State Estimation for Discrete-Time Memristive Recurrent Neural Networks with Stochastic Time-Delays2.1 Problem Formulation2.2 Main Results2.3 An Illustrative Example2.4 Summary 3 Event-Triggered H1 State Estimation for Delayed Stochastic Memristive Neural Networks with Missing Measurements: The Discrete Time Case3.1 Problem Formulation3.2 Main Results3.3 An Illustrative Example3.4 Summary 4 H1 State Estimation for Discrete-Time Stochastic Memristive BAM Neural Networks with Mixed Time-Delays4.1 Problem Formulation and Preliminaries4.2 Main Results4.3 Numerical Example4.4 Summary 5 Stability Analysis for Discrete-Time Stochastic Memristive Neural Networks with Both Leakage and Probabilistic Delays5.1 Problem Formulation5.2 Main Results5.3 Illustrative Examples5.4 Summary 6 Delay-Distribution-Dependent H1 State Estimation for Discrete-Time Memristive Neural Networks with Mixed Time-Delays and Fading Measurements6.1 Problem Formulation6.2 Main Results6.3 Illustrative Examples6.4 Summary 7 On State Estimation for Discrete Time-Delayed Memristive Neural Networks under the WTOD Protocol: A Resilient Set-Membership Approach7.1 Problem Formulation7.2 Main Results7.3 An Illustrative Example7.4 Summary 8 On Finite-Horizon H1 State Estimation for Discrete-Time Delayed Memristive Neural Networks under Stochastic Communication Protocol8.1 Problem Formulation and Preliminaries8.2 Main Results8.3 An Illustrative Example8.4 Summary 9 Resilient H1 State Estimation for Discrete-Time Stochastic Delayed Memristive Neural Networks: A Dynamic Event-Triggered Mechanism9.1 Problem Formulation9.2 Main Results9.3 An Illustrative Example9.4 Summary 10 H1 and l2-l1 State Estimation for Delayed Memristive Neural Networks on Finite Horizon: The Round-Robin Protocol10.1 Problem Formulation and Preliminaries10.2 Main Results10.3 An Illustrative Example10.4 Summary11 Conclusions and Future TopicsBibliographyIndex