Exponential Synchronization of Networked Chaotic Delayed Neural Network by a Hybrid Event Trigger Scheme

Area of Science:

• Chaos theory
• Neural networks
• Control systems

Background:

• Master-slave chaotic delayed neural networks are complex systems.
• Network control introduces challenges like external disturbances and delays.
• Limited communication capacity and bandwidth necessitate efficient control strategies.

Purpose of the Study:

• To achieve exponential synchronization in master-slave chaotic delayed neural networks.
• To develop an event-triggered control scheme that conserves network resources.
• To address external disturbances and network-induced delays within a network control framework.

Main Methods:

• A hybrid event-triggered control scheme is proposed.
• Lyapunov functional is utilized to derive stability criteria.
• The error system is analyzed for (κ, μ, θ)-dissipativity performance.
• Co-design of the event-trigger scheme and controller is performed.

Main Results:

• A sufficient criterion for exponential synchronization is established.
• The proposed hybrid event-triggered control reduces data transmission.
• The Zeno phenomenon is avoided.
• The method ensures stability and synchronization under network constraints.

Conclusions:

• The developed hybrid event-triggered control scheme effectively achieves exponential synchronization.
• The approach conserves network resources and avoids the Zeno phenomenon.
• The results are validated through a numerical example, demonstrating practical applicability.