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Output Synchronization via Intermittent Dynamic Event-Triggered Sampled-Data Security Control for Delayed
IEEE Transactions on Cybernetics
|February 16, 2026
Summary
This study introduces a novel intermittent dynamic event-triggered sampled-data (IDETSD) security control for reaction-diffusion neural networks (RDNNs). The method enhances synchronization under deception attacks and network delays.
Area of Science:
- Control Theory
- Cybersecurity
- Artificial Intelligence
Background:
- Reaction-diffusion neural networks (RDNNs) are crucial in complex system modeling.
- Ensuring secure control in RDNNs is challenging due to delays and cyberattacks.
- Existing time-triggered control methods are less effective against sophisticated attacks.
Purpose of the Study:
- To develop an intermittent dynamic event-triggered sampled-data (IDETSD) security control for RDNNs.
- To achieve output synchronization in RDNNs despite delays and random deception attacks.
- To enhance the resilience of RDNNs against cyber threats.
Main Methods:
- Utilizing a dynamic event-triggered (ET) mechanism to mitigate deception attacks.
- Applying an ET-dependent switched Lyapunov functional (LF) and inequality techniques.
- Designing the IDETSD controller by solving linear matrix inequalities (LMIs).
Main Results:
- Successfully achieved output synchronization for delayed RDNNs under random deception attacks.
- Demonstrated the effectiveness of the dynamic ET mechanism over traditional time-triggered strategies.
- Validated the proposed control method through simulation studies.
Conclusions:
- The proposed IDETSD security control is effective for RDNNs facing delays and deception attacks.
- The dynamic ET approach offers superior mitigation of cyberattacks compared to time-triggered methods.
- The study provides a robust framework for secure and synchronized RDNN control.
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