The Synchronization Analysis of Cohen-Grossberg Stochastic Neural Networks with Inertial Terms

Abstract

The exponential synchronization (ES) of Cohen-Grossberg stochastic neural networks with inertial terms (CGSNNIs) is studied in this paper. It is investigated in two ways. The first way is using variable substitution to transform the system to another one and then based on the properties of $i\hat{t}o$ integral, differential operator, and the second Lyapunov method to get a sufficient condition of ES. The second way is based on the second-order differential equation, the properties of calculus are used to get a sufficient condition of ES. At last, results of the theoretical derivation are verified by virtue of two numerical simulation examples.