中文摘要：

从连续时间动力学的角度，研究了多层前馈神经网络的学习问题。基于李雅普诺夫稳定性分析方法，建立了一种神经网络权重参数连续调整的学习算法，并基于连续时间系统仿真的思想，给出了一种算法实现的自适应策略。算法实现中，通过估计截断误差估计自动调整步长，几乎不需要人工确定任何参数，而且可以保证算法的稳定性及计算精度。最后，给出了两个典型的应用算例。

英文摘要：

The learning issue in feedforward neural networks was investigated and analyzed from the viewpoint of continuous-time system dynamics. A continuous learning algorithm for the multilayer feedforwad networks was set up based on Lyapunov stability theory, and an adaptive updating law for the network weights was presented upon the continuous-time system simulation. In the algorithm implementation, the estimated tnmcation error was applied to adjust the step-size. Almost no preset parameters are required to operate the discrete-time adaptive learning laws, and numerical stability and satisfactory accuracy are assured. Finally, two illustrative examples were given.