Robust stability of delayed reaction–diffusion recurrent neural networks with Dirichlet boundary conditions on time scales

In this paper, the global robust exponential stability of equilibrium solution to delayed reaction–diffusion recurrent neural networks with Dirichlet boundary conditions on time scales is studied. Using topological degree theory, M-matrix method, Lyapunov functional and inequality skills, we establish some sufficient conditions for the existence, uniqueness and global robust exponential stability of equilibrium solution to delayed reaction–diffusion recurrent neural networks with Dirichlet boundary conditions on time scales. One example is given to illustrate the effectiveness of our results.