Global exponential stability of MAM neural network with time delays

By extending the bidirectional associative memory neural network model, a mathematical model of multidirectional associative memory (MAM) neural networks with constant time delays is proposed. By using Brouwer fixed point theorem and the upper right Dini derivative, a sufficient condition for the existence and the global exponential stability of an equilibrium point is obtained. And for a special MAM neural network which connection weights is positive, a sufficient and necessary condition for the existence and the global exponential stability of an equilibrium point is obtained. The results are new for MAM neural networks. An example and its numerical simulation are given to illustrate the effectiveness of the obtained results.