Exponential stability of reaction-diffusion Cohen-Grossberg neural networks with variable coefficients and distributed delays

This paper is devoted to investigation of the stability of reaction-diffusion Cohen-Grossberg neural networks with variable coefficients and distributed delays. By employing the method of variational parameter and inequality technique, delay independent and easily verifiable sufficient conditions to guarantee the exponential stability of an equilibrium solution associated with temporally uniform external inputs are obtained, without assuming the monotonicity and differentiability of activation functions, nor symmetry of synaptic interconnection weights. An example is provided to illustrate our theoretical results.