Exponential stability of Cohen-Grossberg neural networks with multiple time-varying delays and distributed delays

Maybe because Cohen-Grossberg neural networks with multiple time-varying delays and distributed delays cannot be converted into the vector-matrix forms, the stability results of such networks are relatively few and the stability conditions in the linear matrix inequality forms have not been established. So this paper investigates the exponential stability of the networks and gives the sufficient condition in the linear matrix inequality forms. Two examples are provided to demonstrate the effectiveness of the theoretical results.

Automated summary

Due to the inability to convert Cohen-Grossberg neural networks with multiple time-varying delays and distributed delays into vector-matrix forms, the stability results and conditions in the linear matrix inequality forms are relatively few and missing. This paper addresses the issue by investigating the exponential stability of the networks and providing sufficient conditions in the linear matrix inequality forms. Two examples are used to demonstrate the effectiveness of the theoretical results.