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Asynchronous dissipative filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts
Summary
This study addresses asynchronous filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts. Novel resilient filters are developed using a hidden Markov model approach for enhanced generality and performance.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Artificial Intelligence
Background:
- Nonhomogeneous Markov switching neural networks are crucial for modeling complex systems.
- Variable packet dropouts (VPDs) introduce significant challenges in network control.
- Asynchronous operation between systems and filters degrades performance.
Purpose of the Study:
- To develop robust asynchronous filtering techniques for nonhomogeneous Markov switching neural networks.
- To account for time-varying transition probabilities and mode-dependent VPDs.
- To enhance filter generality by addressing synchronization issues.
Main Methods:
- Utilizing polytope technology to reveal time-varying transition probabilities.
- Modeling variable packet dropouts using a Bernoulli distributed sequence.
- Formulating a hidden Markov model to capture asynchronization between networks and filters.
Main Results:
- Designed resilient filters that are more general than existing methods.
- Effectively handled mode-dependent and variable packet dropout rates.
- Demonstrated the effectiveness of the proposed filter scheme through simulations.
Conclusions:
- The developed asynchronous filtering scheme provides a robust solution for nonhomogeneous Markov switching neural networks with VPDs.
- The hidden Markov model approach successfully addresses synchronization issues.
- The proposed filters offer improved performance and generality in networked control systems.
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