考察了延迟连续Ｈｏｐｆｉｅｌｄ神经网络的渐近稳定性质及其学习算法，给出了该神经网络渐近稳定的充分条件，提出了相应的学习规则和算法．按该模式进行了应用实例的计算机模拟，结果表明，使用延迟神经网络可以提高网络学习速度 The asymptotic stability of delayed continuous Hopfield neural networks and its learning algorithm are investigated. The sufficient conditions for the asymptotic stability of the neural network are given. Corresponding learning rules and algorithms are proposed. According to this model, the computer simulation of application examples shows that the use of delayed neural network can improve the speed of network learning