Convergence of asymptotic systems in Cohen-Grossberg neural network models with unbounded delays

TL;DR

This paper studies the convergence behavior of non-autonomous Cohen-Grossberg neural networks with unbounded delays, establishing stability conditions when non-delay influences dominate, supported by examples and simulations.

Contribution

It provides new stability results for neural networks with infinite delays, emphasizing conditions where non-delay terms lead to convergence.

Findings

Stability achieved when non-delay terms dominate delays

Conditions for convergence in networks with unbounded delays

Numerical simulations validate theoretical results

Abstract

In this paper, we investigate the convergence of asymptotic systems in non-autonomous Cohen--Grossberg neural network models, which include both infinite discrete time-varying and distributed delays. We derive stability results under conditions where the non-delay terms asymptotically dominate the delay terms. Several examples and a numerical simulation are provided to illustrate the significance and novelty of the main result.