Stabilizing Effects of Impulses in Delayed BAM Neural Networks

This brief studies the stabilizing effects of impulses in delayed bidirectional associative memory (DBAM) neural networks when its continuous component does not converge asymptotically to the equilibrium point. A general criterion, which characterizes the aggregated effects of the impulse and the deviation of its continuous component from the equilibrium point on the exponential stability of the considered DBAM, is established by using Lyapunov-Razumikhin technique. It is shown that because of shrinking effects of impulse the DBAM may be globally exponentially stable even if the evolution of its continuous component deviates from the equilibrium point.