pth moment exponential stability of stochastic fuzzy Cohen–Grossberg neural networks with discrete and distributed delays

In this paper, stochastic fuzzy Cohen–Grossberg neural networks with discrete and distributed delays are investigated. By using Lyapunov function and the Ito differential formula, some sufficient conditions for the pth moment exponential stability of such stochastic fuzzy Cohen–Grossberg neural networks with discrete and distributed delays are established. An example is given to illustrate the feasibility of our main theoretical findings. Finally, the paper ends with a brief conclusion. Methodology and achieved results is to be presented

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