Hongjian Liu, Zidong Wang, Lifeng Ma

Stability Analysis and State Estimation of Memristive Neural Networks (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 234
• Erscheinungstermin: 16. August 2021
• Englisch
• ISBN-13: 9781000415001
• Artikelnr.: 62167450

Autorenporträt

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.

Inhaltsangabe

1. Introduction. 2. H1 State Estimation for Discrete
Time Memristive Recurrent Neural Networks with Stochastic Time
Delays. 3. Event
Triggered H1 State Estimation for Delayed Stochastic Memristive Neural Networks with Missing Measurements: The Discrete Time Case. 4. H1 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 H1 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 H1 State Estimation for Discrete
Time Delayed Memristive Neural Networks under Stochastic Communication Protocol. 9. Resilient H1 State Estimation for Discrete
Time Stochastic Delayed Memristive Neural Networks: A Dynamic Event
Triggered Mechanism. 10. H1 and l2
l1 State Estimation for Delayed Memristive Neural Networks on Finite Horizon: The Round
Robin Protocol.