Adaptive event-triggered extended dissipative synchronization of delayed reaction-diffusion neural networks under...

Area of Science:

• Control Theory
• Neural Networks
• Network Security

Background:

• Delayed reaction-diffusion neural networks are crucial in modeling complex systems.
• Ensuring output synchronization under disturbances like spatially averaged measurements (SAMs) and deception attacks is challenging.
• Existing event-triggered sampled-data control methods often use fixed thresholds, limiting adaptability.

Purpose of the Study:

• To develop an adaptive event-triggered sampled-data (AETSD) control strategy for extended dissipativity output synchronization.
• To address synchronization issues in delayed reaction-diffusion neural networks under SAMs and deception attacks.
• To design a control scheme that conserves limited transmission channels and is suitable for resource-constrained systems.

Main Methods:

• Proposed an adaptive event-triggered sampled-data (AETSD) control scheme.
• Utilized Lyapunov-Krasovskii functionals and inequality techniques to establish synchronization criteria.
• Employed linear matrix inequalities (LMIs) for controller design.

Main Results:

• Developed novel synchronization criteria for delayed reaction-diffusion neural networks under SAMs and deception attacks.
• Designed an AETSD controller that adaptively adjusts thresholds based on current signals.
• Demonstrated the controller's effectiveness in achieving extended dissipativity behaviors via a numerical example.

Conclusions:

• The proposed AETSD control strategy effectively achieves extended dissipativity output synchronization for delayed reaction-diffusion neural networks.
• The adaptive nature of the controller enhances efficiency and robustness against SAMs and deception attacks.
• The method provides a valuable approach for secure and efficient control in networked systems with limited resources.