Random Attractor of Reaction-Diffusion Hopfield Neural Networks Driven by Wiener Processes

Abstract

This paper studies the global existence and uniqueness of the mild solution for reaction-diffusion Hopfield neural networks (RDHNNs) driven by Wiener processes by applying a Schauder fixed point theorem and a priori estimate; then the random attractor for this system is also studied by constructing proper random dynamical system.