Stability of Analytic Neural Networks With Event-Triggered Synaptic Feedbacks

Area of Science:

• Artificial Intelligence
• Computational Neuroscience
• Control Theory

Background:

• Traditional neural networks face computational and transmission burdens.
• Event-triggered mechanisms offer a solution by reducing data exchange.
• This work generalizes existing models, including the Hopfield neural network.

Purpose of the Study:

• To investigate the stability of analytic neural networks with event-triggered synaptic feedback.
• To demonstrate the efficiency of event-triggered rules in reducing computational load.
• To analyze the convergence properties of such networks.

Main Methods:

• Utilizing event-triggered control rules for synaptic feedback.
• Applying the Łojasiewicz inequality to prove trajectory convergence.
• Verifying the absence of Zeno behaviors to ensure practical implementation.

Main Results:

• Analytic neural networks with event-triggered rules exhibit guaranteed convergence to equilibrium.
• The event-triggered approach significantly reduces computational and transmission loads.
• The proposed method is shown to be free from Zeno phenomena.

Conclusions:

• Event-triggered rules provide an effective method for enhancing the stability and efficiency of analytic neural networks.
• The findings offer a theoretical foundation for designing more efficient neural network systems.
• The study confirms the practical feasibility of event-triggered mechanisms in neural network applications.