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

Abstract

AbstractIn this article, we investigate exponential lag synchronization results for the Cohen–Grossberg neural networks 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.