Practical Fixed-Time Bipartite Synchronization of Uncertain Coupled Neural Networks Subject to Deception Attacks via...

Area of Science:

• Control Theory
• Network Science
• Applied Mathematics

Background:

• Coupled neural networks are crucial in various applications but often face uncertainties and communication constraints.
• Event-triggered control strategies aim to reduce communication load in networked systems.
• Bipartite synchronization, involving cooperative and antagonistic interactions, presents a complex synchronization challenge.

Purpose of the Study:

• To investigate practical fixed-time synchronization for uncertain coupled neural networks.
• To develop a dual-channel event-triggered control strategy to minimize communication and control updates.
• To analyze bipartite synchronization under deception attacks in communication channels.

Main Methods:

• Utilizing Lyapunov and comparison theories to establish synchronization criteria.
• Designing novel dual-channel event-triggered mechanisms for sensor-to-controller and controller-to-actuator links.
• Modeling deception attacks using Bernoulli's stochastic variables.

Main Results:

• Achieved fixed-time bipartite synchronization for uncertain coupled neural networks.
• Developed an event-triggered control strategy that significantly reduces communication and controller update frequency.
• Derived explicit expressions for the settling time of the synchronization process.
• Demonstrated the effectiveness of the proposed method through a Chua's circuit system example.

Conclusions:

• The proposed dual-channel event-triggered control is effective for practical fixed-time bipartite synchronization of uncertain coupled neural networks.
• The strategy successfully mitigates communication overhead while ensuring robust synchronization under deception attacks.
• The theoretical results are validated by a practical circuit system simulation, confirming the method's feasibility.