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.
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.
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.
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.
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