Global Robustly Asymptotically Stability of Cohen-Grossberg Neural Networks with Nonnegative Amplification Function

Abstract

The global robust asymptotic stability problem of Cohen-Grossberg neural networks with nonnegative amplification function is considered in this paper. The amplification function condition is assumed to be nonnegative. In the terms of linear matrix inequalities (LMIs), sufficient conditions are obtained by using Lyapunov- Krasovskii method which guarantee the existence and global robustly asymptotic stability of the equilibrium point of the Cohen-Grossberg neural networks with nonnegative amplification function. Finally, a numerical example is provided to verify the effectiveness of the proposed results.