Exponential stability of stochastic higher-order BAM neural networks with reaction–diffusion terms and mixed time-varying delays

In this paper, we investigate the exponential stability of stochastic reaction–diffusion Bi-directional Associative Memory (BAM) neural networks. By constructing a novel Lyapunov–Krasovskii function, and applying inequality analysis technique as well as M-matrix theory, we first give some sufficient exponential stability criteria in terms of p-norm for a class of high-order stochastic reaction–diffusion BAM neural networks with discrete and distributed delays. The model we formulated is new and more general than the BAM neural networks investigated in previous publications. Moreover, the obtained results are easy to check and improve some existing stability results. An example is presented to show the application of the criteria obtained in this paper.