基于BP神经网络的食品安全抽检数据挖掘

Data Mining on Food Safety Sampling Inspection Data Based on BP Neural Network

数据挖掘技术在食品安全领域拥有巨大的应用价值和潜力。通过分析逆向传播(BP)神经网络算法,说明使用该方法的可行性和优越性。以抽检数据为对象,阐述了数据预处理过程,设计并实现了数据挖掘实验。最后利用挖掘结果进行食品检验结论预测,验证了方法的实用价值和指导意义。实验表明,基于BP神经网络的数据挖掘方法具有良好的过程健壮性和较高的结果准确性。通过预判不合格食品的出现,可以指导实际食品安全抽检工作,从而杜绝食品安全问题的发生。

Data mining technology has great application values and potential in the food safety field. The feasibility and advantage of the BP neural network algorithm were explained. The process of data prepro-cessing was introduced, and the experiment of data mining was designed then realized, focusing on sam-pling inspection data. Finally, by taking advantage of the mining results, a prediction of food inspection conclusions was put forward which verified the method’ s practical value and guiding significance. The experiment indicated that data mining method based on BP neural network has favorable robustness and good accuracy. The predictions of unqualified food’ s appearance can lead food safety sampling and in-spection work in practice, which can put an end to the occurrence of food safety problems.