Abstract：The main issue about “black box” inherent in artificial neural networks (ANN’s) is discussed. Adding transparency is well recognized to be an effective solution to dealing with this problem. Significant benefits are obtained through using this approach, such as providing a certain degree of comprehensive power, decreasing model size, speeding learning process and improving generalization capability. A hierarchical classification is applied to the existing approaches for better understanding of their intrinsic features and limitations. The first level of classification is made by two strategies: building prior knowledge into neural networks; extracting rules embedded within networks. Most of important approaches are introduced and compared in detail with further classifications within each strategy. Finally, the personal perspectives to the studies of machine learning are presented. Other objective functions are suggested for the extension of studies, such as performancetocost ratio and transparency. The study of increasing transparency to ANN’s is considered as the most fundamental and direct solution to the other existing issues. A new machine learning approach called Knowledge Increasing via Feedback is proposed.
胡包钢，王泳，杨双红，曲寒冰. 如何增加人工神经元网络的透明度?*[J]. 模式识别与人工智能, 2007, 20(1): 72-84.
HU BaoGang, WANG Yong, YANG ShuangHong, QU HanBing. How to Add Transparency to Artificial Neural Networks. , 2007, 20(1): 72-84.
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