Global exponential convergence for a class of HCNNs with neutral time-proportional delays

This paper is concerned with a class of high-order cellular neural networks with neutral time-proportional delays. Based on a new differential inequality technique, some sufficient conditions are derived to ensure that all solutions of the addressed system converge exponentially to zero vector, which improve and supplement existing ones. Also, an example and its numerical simulations are given to demonstrate our theoretical results.