Inequalities and Stability of Stochastic Fuzzy Delayed Cohen-Grossberg Neural Networks

Stability is an important indicator for evaluating complex dynamic systems’ performance. Many problems in practice are abstracted into the stability of networks. This study examines stochastic fuzzy Cohen-Grossberg neural networks(CGNNs) with delayed $p$ th moment exponential stability and almost sure exponential stability. It is an improvement and supplement to existing work. Our method is based on integral inequality, differential inequality, stochastic analysis theory and Itô’s formula, which discusses the system’s stability, we have obtained sufficient conditions for system stability, which avoided the construction of complex Lyapunov functions. Moreover, our method does not require that the activation function be bounded, differentiable and monotone, and provides sufficient con $\cdot $ editions decreased conservative. At the same time, it is verified that fuzzy and stochastic terms have positive effects on system stability. Finally, the effectiveness of the results is verified by a simulation example.