An outlier identification and judgment method for an improved neural-network BOF forecasting model

作者：Feng Mingxia^*; Li Qiang; Zou Zongshu

来源：Steel Research International, 2008, 79(5): 323-332.

DOI：10.2374/SRI07SP051-2008-323

• BOF forecasting model
• neural network
• data pretreatment
• outlier identification
• outlier judgment

Due to the lack of simple and effective data filtering method for multi-variable and numerous samples in BOF endpoint forecasting model, a method of outlier identification and judgment was introduced and applied to data screens for improving BOF endpoint forecasting model. The outside values as potential outliers are calculated using the method of five-number summary which is a robust estimation of the population parameter, and then the potential outliers are judged with the clustering method. By comparing the exceptional data from clustering analysis with the outside values from the five-number summary, the intersection of these two groups is regarded as the final outliers to be deleted; in addition, the exceptional data but not outside values are regarded as final exceptional data to be further analyzed; and the outside values but not exceptional data are regarded as final outliers to be deleted too. Finally, to verify the data selection, an improved BP-based neural network model is used to predict the end-point carbon content and temperature. By using this data pretreatment method, the absolute values of the mean and maximum training residuals of endpoint carbon and temperature decreased by 26.7%, 41 % and 17.3%, 34.5% respectively; and those of the prediction decreased by 10%, 44.9% and 9.4%, 22.9% respectively. It is shown that the proposed method improves effectively the neural network model for BOF endpoint forecasting.

• 出版日期2008-5
• 单位辽宁科技学院; 东北大学