LMI Approach to Stability Analysis of Cohen-Grossberg Neural Networks with p -Laplace Diffusion

Abstract

The nonlinear $p$-Laplace diffusion ($p>1$) was considered in the Cohen-Grossberg neural network (CGNN), and a new linear matrix inequalities (LMI) criterion is obtained, which ensures the equilibrium of CGNN is stochastically exponentially stable. Note that, if $p=2$, $p$-Laplace diffusion is just the conventional Laplace diffusion in many previous literatures. And it is worth mentioning that even if $p=2$, the new criterion improves some recent ones due to computational efficiency. In addition, the resulting criterion has advantages over some previous ones in that both the impulsive assumption and diffusion simulation are more natural than those of some recent literatures.