神经网络规则抽取

神经网络是一种黑箱模型，其学习到的知识蕴涵在大量连接权中，不仅影响了用户对利用神经计算技术构建智能系统的信心，还阻碍了神经网络技术在数据挖掘领域的应用，由于对神经网络规则抽取进行研究有助于解决上述问题，因此该领域已成为机器学习和神经计算界的研究热点，介绍了神经网络规则抽取研究的历史，综述了国际研究现状，对关于这方面研究的不同看法进行了讨论，并指出该领域中一些值得进一步研究的内容。

RULE EXTRACTION FROM NEURAL NETWORKS

Neural network is a blackbox model whose learned knowledge is concealed in a large amount of connections. This has not only weakened the confidence of users in building intelligent systems using neural computing techniques, but also hindered the application of neural networks to data mining. Since extracting rules from neural networks help to solve those problems, this area has become a hot topic in both machine learning and neural computing communities. In this paper, the history of rule extraction from neural networks is introduced, the state-of-the-art of this field is surveyed, some controversies are discussed, and some issues valuable for future exploration in this area is indicated.