Global asymptotic and exponential stability of a dynamic neural system with asymmetric connection weights

Recently, a dynamic neural system was presented and analyzed due to its good performance in optimization computation and low complexity for implementation. The global asymptotic stability of such a dynamic neural system with symmetric connection weights was well studied. In this note, based on a new Lyapunov function, we investigate the global asymptotic stability of the dynamic neural system with asymmetric connection weights. Since the dynamic neural system with asymmetric weights is more general than that with symmetric ones, the new results are significant in both theory and applications. Specially, the new result can cover the asymptotic stability results of linear systems as special cases.