Abstract: For the high dimensional chemical engineering processing data, an outlier detection method based on self-organizing map (SOM)networks and its visualization methods was proposed. Practically, it was applied for the observed data of preflash tower and the satisfactory result was obtained. Firstly, SOM was applied to obtain the topology-preserving plane for the high dimensional data. Then, based on the mapping plane and its visualization methods, the outliers were visualized clearly and easily. The results show that the proposed method does not need complex calculation, and the outliers in high dimensional data are effectively detected and eliminated.

Key words: SOM networks ,  Outlier detection , Visualization , Preflash tower

摘要： 针对石油化工生产过程样本数据呈高维的特征, 提出了基于自组织映射(Self -Orga nizing M ap,
SOM)网络的粗差判别方法, 并实际应用于初馏塔生产过程。首先应用SOM 网络对初馏塔生产过程数据进行保留
拓扑结构的降维映射, 然后通过对其映射平面神经元间距离的可视化分析, 实现数据粗差判别。研究结果表明用
SOM 网络来发现高维复杂生产过程数据中的粗差具有很好的可视化效果及应用前景

关键词: SOM 网络 , 粗差判别 , 可视化 , 初馏塔