Exponential stability of stochastic higher-order BAM neural networks with reactiondiffusion terms and mixed time-varying delays
In this paper, we investigate the exponential stability of stochastic reactiondiffusion Bi-directional Associative Memory (BAM) neural networks. By constructing a novel LyapunovKrasovskii 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 reactiondiffusion 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.
exponential stability reaction-diffusion stochastic bam neural networks discrete and distributed time-varying delays