Exponential stability of reaction–diffusion generalized Cohen–Grossberg neural networks with time-varying delays

The stability property of reaction–diffusion generalized Cohen–Grossberg neural networks (GDCGNNs) with time-varying delay are considered. Without assuming the monotonicity and differentiability of activation functions, nor symmetry of synaptic interconnection weights, delay independent and easily verifiable sufficient conditions to guarantee the exponential stability of an equilibrium solution associated with temporally uniform external inputs to the networks are obtained, by employing the method of variational parameter and inequality technique. One example is given to illustrate the theoretical results.