提出了一种根据粗糙集理论进行ＢＰ网络设计的方法，它结合了粗糙集理论的强大的定性分析能力和ＢＰ网络的准确的逼近能力，得到一种可理解性好、计算简单、收敛速度快的神经网络模型．这种神经网络的学习算法的要点是：应用粗糙集的理论和方法，从给定学习样本数据中发现一组规则，并根据这些规则去建立网络模型中相应的隐层节点；然后用ＢＰ算法迭代求出网络的参数，从而完成网络的设计.

A designing method for BP neural networks based on rough set theory is presented in this paper. Rough set theory has a powerful capability for qualitative analysis, while BP neural networks can approach most problems with a much satisfying accuracy. By combining those advantages of the two theories, we can construct a kind of neural network with good understandability, simple computation and exact accuracy. The key idea of the learning algorithm for the proposed neural network is as follows: find a set of r...