Abstract: The XOR question can’t be implemented by the structure and study regulation of feeling machines，started with which，this production uses BP network to solve the XOR question，which obliterates the limitation of the feeling machine.But problems come into existence in concrete apply of BP network，for example：The restrained rate is slow，and there is existence of the strong coupling relation with other parameters.Otherwise the partial is extreme minute.So according to the mechanism of feed-forward neural network，it puts forward a method with self-adaptive learning rate factors for the improvement of BP algorithm.The improved algorithm is applied to the learning of two or more dimensions XOR question.The simulations show the improved algorithm has good effects on speeding up learning process and bettering its learning convergence and robust performance.

Key words: neural networks, XOR, self-adaptive learning rate, back propagation

摘要： 从感知器的结构及学习规则无法执行异或问题出发，用神经网络中的BP网络来解决异或问题，消除了感知器的局限性，但BP算法在具体实现中常会出现一些问题，如：收敛速度缓慢且与其他参数存在较强的耦合关系，局部极小等。对此，从前馈神经网络的原理出发，提出了一种自适应学习速率因子方法，用于对BP算法的改进，并将改进后的算法用于二维XOR问题及多维XOR问题的学习中。仿真实验证明，改进后的算法可显著提高网络的学习速度，且学习过程具有良好的收敛性及较强的鲁棒性。

关键词: 神经网络, 异或, 自适应学习速率, 反向传播