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Sampling-Based Event-Triggered Exponential Synchronization for Reaction-Diffusion Neural Networks.

Qian Qiu, Housheng Su

IEEE Transactions on Neural Networks and Learning Systems

|August 25, 2021

Summary

This summary is machine-generated.

This study introduces a novel event-triggered control strategy for reaction-diffusion neural networks (RDNNs). The method reduces communication load and avoids Zeno behavior, ensuring efficient exponential synchronization.

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Area of Science:

Control theory
Applied mathematics
Computational neuroscience

Background:

Reaction-diffusion neural networks (RDNNs) are crucial for modeling complex spatio-temporal dynamics.
Event-triggered control schemes aim to reduce communication and computational burdens in networked systems.
Dirichlet boundary conditions are frequently encountered in physical and biological models.

Purpose of the Study:

To develop an effective sampling-based event-triggered control strategy for achieving exponential synchronization in RDNNs.
To minimize communication frequency and energy consumption by updating control protocols only when necessary.
To ensure the avoidance of the Zeno phenomenon in the proposed control system.

Main Methods:

Utilizing a sampling-based event-triggered control scheme.
Designing a control protocol that updates based on sampled state information and a triggering condition.
Employing Lyapunov-Krasovskii functionals and inequality techniques to establish synchronization conditions.
Integrating sampled-data control principles to prevent Zeno behavior.

Main Results:

A sufficient condition for achieving exponential synchronization in RDNNs under Dirichlet boundary conditions was derived.
The proposed event-triggered control strategy effectively reduces communication load and conserves energy.
The combined approach with sampled-data control successfully avoids the Zeno phenomenon.
Simulation results validated the efficacy and practicality of the developed control algorithm.

Conclusions:

The proposed sampling-based event-triggered control is a viable and efficient method for achieving exponential synchronization in RDNNs.
This approach offers significant advantages in terms of communication efficiency and energy saving for distributed systems.
The study provides a robust theoretical framework and practical validation for advanced control of neural network models.