Robust Global Exponential Stability for Interval Reaction–Diffusion Hopfield Neural Networks With Distributed Delays

Abstract

This brief presents a sufficient condition for the existence, uniqueness, and robust global exponential stability of the equilibrium solution for a class of interval reaction diffusion Hopfield neural networks with distributed delays and Dirichlet boundary conditions by constructing suitable Lyapunov functional and utilizing some inequality techniques. The result imposes constraint conditions on the boundary values of the network parameters. The result is also easy to verify and plays an important role in the design and application of globally exponentially stable neural circuits.