Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices

Area of Science:

• Control Theory
• Artificial Neural Networks
• Nonlinear Systems

Background:

• Existing drive-response neural networks lack effective synchronization control for state-dependent coefficients.
• Synchronization is crucial for various applications, including secure communication and complex system modeling.

Purpose of the Study:

• To propose a novel drive-response neural network model.
• To develop a robust synchronization control strategy for these networks.
• To ensure uniformly ultimately bounded (UUB) synchronization.

Main Methods:

• Introduction of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov functions.
• Application of the convex hull Lyapunov function approach for UUB synchronization design.
• Construction of a delay-independent control law formulated using bilinear matrix inequalities.

Main Results:

• A novel drive-response neural network model is established.
• The proposed control law guarantees bounded synchronization for the new network model.
• Conditions for synchronization are less conservative compared to existing literature.

Conclusions:

• The developed convex hull Lyapunov function approach effectively achieves UUB synchronization for the novel neural network model.
• The delay-independent control law provides a robust solution for bounded synchronization.
• The findings offer a less conservative and more suitable method for synchronizing state-dependent coefficient neural networks.