Global exponential convergence of non-autonomous cellular neural networks with multi-proportional delays

The paper is concerned with the exponential convergence for a class of non-autonomous cellular neural networks with multi-proportional delays. By employing the differential inequality techniques, we establish a novel result to ensure that all solutions of the addressed system converge exponentially to zero vector. Our results complement with some recent ones. Moreover, an illustrative example and its numerical simulation are given to demonstrate the effectiveness of the obtained results.