Synchronization for an Array of Coupled Cohen-Grossberg Neural Networks with Time-Varying Delay

Abstract EN

This paper makes some great attempts to investigate the global exponential synchronization for arrays of coupled delayed Cohen-Grossberg neural networks with both delayed coupling and one single delayed one.

By resorting to free-weighting matrix and Kronecker product techniques, two novel synchronization criteria via linear matrix inequalities (LMIs) are presented based on convex combination, in which these conditions are heavily dependent on the bounds of both the delay and its derivative.

Owing to that the addressed system can include some famous neural network models as the special cases, the proposed methods can extend and improve those earlier reported ones.

The efficiency and applicability of the presented conditions can be demonstrated by two numerical examples with simulations.