Existence and global exponential stability of periodic solution for discrete-time BAM neural networks

The discrete-time analogues of bidirectional associative memory neural networks with periodic coefficients and distributed delays are formulated and studied. And by using the continuation theorem of coincidence degree theory, we derive the existence of periodic solution for the discrete-time BAM neural networks. And by constructing a appropriate Lyapunov-type sequence, we prove the global exponential stability of the periodic solution for the model. It is shown that the discrete-time analogues inherit the existence and global exponential stability of periodic solution for the continuous-time BAM neural networks. An example is given to illustrate the effectiveness of the obtained results.