Global exponential periodicity for BAM neural network with periodic coefficients and continuously distributed delays

By constructing a suitable Lyapunov function and using some analysis techniques, rather than employing the continuation theorem of coincidence degree theory as in other literature, a sufficient criterion is obtained to ensure the existence and global exponential stability of periodic solution for the bidirectional associative memory neural network with periodic coefficients and continuously distributed delays. The obtained result is less restrictive to the BAM neural network than the previously known criteria. And it can be applied to the BAM neural network in which signal transfer functions are neither bounded nor differentiable. In addition, an example and its numerical simulation are given to illustrate the effectiveness of the obtained result.