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Full Description
Recursive Filtering of Networked Systems with Communication Protocol Scheduling
explores protocol-based state estimation for complex networked systems including Kalman filtering for nonlinear systems under RR and MEF-TOD protocols, finite-horizon robust state estimation under FlexRay protocol, robust filtering for multi-rate systems under Round-Robin and stochastic communication protocols, and state estimation under event-triggering protocol and redundant channel for neural networks. This book provides theoretical frameworks using techniques like backward Riccati equations, Kalman filtering theory, Unscented transform, and ellipsoid estimation theory.
Features
1 Focuses on the intersection of recursive filtering techniques and communication protocol scheduling
2 Introduces communication protocols to allocate limited network resources
3 Enhances the reliability and accuracy of information transmission and minimize the occurrence of information conflicts and congestion in the network
4 Offers practical strategies for real-world challenges in networked systems
5 Explains designing filtering unit for a class of complex networks
This book is aimed at graduate students and researchers in electrical engineering, power systems, control systems.
Contents
1.Introduction 2. Protocol-Based Unscented Kalman Filtering in the Presence of Stochastic Uncertainties 3. Protocol-Based Extended Kalman Filtering with Quantization Effects: The Round-Robin Case 4. Recursive Set-Membership State Estimation over A FlexRay Network 5. Finite-Horizon H-infinite Filtering via a High-Rate Network with the FlexRay Protocol 6. On Quantized H-infinite Filtering for Multi-Rate Systems Under Stochastic Communication Protocols: The Finite-Horizon Case 7. Distributed Set-Membership Filtering for Multi-Rate Systems Under the Round-Robin Scheduling over Sensor Networks 8. Partial-Neurons-Based State Estimation for Delayed Neural Networks with State-Dependent Noises under Redundant Channels 9. Dynamic Event-Based State Estimation for Delayed Artificial Neural Networks with Multiplicative Noises: A Gain-Scheduled Approach 10. Conclusions and Future Topics
