New results for exponential stability of complex-valued memristive neural networks with variable delays

This paper studies the dynamic behaviors of complex-valued memristive neural networks with variable delays. By using M-matrix, redinequality technique and Lyapunov function, sufficient conditions are proposed to guarantee the existence, uniqueness and global exponential stability for the equilibrium point of complex-valued memristive neural networks with variable delays. The proposed results are easy to be checked and are helpful in qualitative analysis for some complex-valued nonlinear delayed systems. Two numerical examples are given to demonstrate the effectiveness of theoretical results.