Stochastic stability of Hopfield neural networks disturbed by more general noises

This paper addresses the stability analysis of Hopfield neural networks (HNNs) disturbed by more general noises, which consist of continuous and jump random noise (JRN). By introducing Brownian motion (BM) and Poisson process (PP) to describe continuous and jump random noises respectively, this paper presents the model of this kind of stochastic HNNs. Then, this paper utilizes semi-martingale theory to transform the Ito formula into an equivalent form. Based on this, this paper proposes a stability condition by the linear matrix inequality (LMI). Finally, a numerical example is provided to show the effectiveness of the condition.