Novel mean square exponential stability criterion of uncertain stochastic interval type-2 fuzzy neural networks with multiple time-varying delays

This paper investigates the problem of mean square exponential stability for uncertain stochastic interval type-2 (IT2) fuzzy neural networks with multiple time-varying delays. First, IT2 fuzzy neural network is introduced, which takes time delays and parameter uncertainties into account. Compared with the existing results, our model is more applicable since time delays and parameter uncertainties are very common due to environmental and artificial factors. Then, on the basis of a Lyapunov-Krasovskii functional (LKF), stochastic analysis approach, and Ito's differential formula, a new sufficient condition is derived to guarantee the mean square exponential stability of the considered IT2 fuzzy neural network. Finally, a numerical example is provided to show the effectiveness of the proposed criterion.