Exponential Lag Synchronization of Cohen-Grossberg Neural Networks with Discrete and Distributed Delays on Time Scales

TL;DR

This paper establishes exponential lag synchronization for Cohen-Grossberg neural networks with delays on arbitrary time domains using a unified approach, broadening applicability across different time scales.

Contribution

It introduces a unified framework using time scales theory, matrix-measure, and Halanay inequality to analyze neural network synchronization with delays.

Findings

Results are unified and generalize existing synchronization conditions.

Simulation examples confirm effectiveness on various time domains.

Analytical conditions ensure exponential lag synchronization.

Abstract

In this article, we investigate exponential lag synchronization results for the Cohen-Grossberg neural networks (C-GNNs) with discrete and distributed delays on an arbitrary time domain by applying feedback control. We formulate the problem by using the time scales theory so that the results can be applied to any uniform or non-uniform time domains. Also, we provide a comparison of results that shows that obtained results are unified and generalize the existing results. Mainly, we use the unified matrix-measure theory and Halanay inequality to establish these results. In the last section, we provide two simulated examples for different time domains to show the effectiveness and generality of the obtained analytical results.