Global Exponential Stability of MAM Neural Network with Time-Varying Delays

A mathematical model of multidirectional associative memory(MAM) neural networks with varying time delays is proposed. By using Brouwer fixed point theorem, a sufficient condition for the existence of an equilibrium point is obtained. And by constructing a suitable Lyapunov function, a sufficient condition for the global exponential stability of an equilibrium point is obtained, which depends on delays. The results are new for multidirectional associative memory neural networks. An example and its numerical simulation are given to illustrate the effectiveness of the obtained results.