Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay

We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM) neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks.