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Dynamic Event-Triggered State Estimation for Markov Jump Neural Networks With Partially Unknown Probabilities
Summary
This study introduces a dynamic event-triggered asynchronous state estimator for Markov jump neural networks, ensuring finite-time boundedness and performance despite mode-dependent uncertainties and communication constraints.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Artificial Intelligence
Background:
- Markov jump neural networks (MJNNs) present challenges in state estimation due to mode-dependent uncertainties and asynchronous mode transitions.
- Existing state estimators struggle to synchronize with system modes, leading to performance degradation.
- Limited network bandwidth and computational resources necessitate efficient data transmission strategies.
Purpose of the Study:
- To design a finite-time dissipative state estimator for MJNNs that accounts for asynchronous mode transitions.
- To develop a novel dynamic event-triggered transmission mechanism to conserve network resources.
- To guarantee finite-time boundedness and a specified dissipation performance index for the estimation error.
Main Methods:
- Utilizing a hidden Markov model with partly unknown probabilities to characterize asynchronous mode transitions.
- Implementing a dynamic event-triggered transmission mechanism with an adjustable diagonal matrix threshold.
- Applying Lyapunov stability theory to design the event-based asynchronous state estimator.
Main Results:
- The proposed estimator ensures the resulting estimation error system is finite-time bounded.
- A prescribed dissipation performance index is achieved for the estimation error.
- The dynamic event-triggered mechanism effectively reduces data transmission while maintaining estimation performance.
Conclusions:
- The developed event-based asynchronous state estimator is effective for finite-time dissipative state estimation in MJNNs.
- The dynamic event-triggered mechanism offers a practical solution for resource-constrained networked systems.
- Numerical simulations validate the proposed approach's performance and efficiency.
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