基于神经网络的食品安全评价模型构建研究

Research on Establishment of Food Safety Evaluation Model Based on Neural Network

食品安全评价模型的准确度高低,直接影响食品安全状况评价、预测的准确率.结合危害分析与关键控制点的(HACCP)食品安全管理体系理论,从食品供应链的角度出发,建立食品安全评价指标体系;使用层次分析法(AHP)改进逆向传播(BP)神经网络算法中随机初始化计算权重的方法,训练样本数据,并以测试数据作为验证,检测模型的误差收敛速度和拟合度.结果表明,这种BP神经网络结合AHP方法构建的模型具有实用、精度高、快速、客观等优点,可用于生产、加工、销售等流通环节食品安全评价、区域食品安全评价以及种类食品安全评价.

The accuracy of the food safety evaluation model directly influences the accuracy of food safety situation assessment and forecast. Based on the hazard analysis critical control point theory （HACCP） , a food safety evaluation index system was established from the perspective of the food supply chain. In order to detect the convergence speed and fitting degree of the model＇ s deviation, the analytic hierarchy process was utilized to improve the random initialization calculating weight method in the backward propa- gation neural network algorithm. Meanwhile, the sample data were trained and the test data were valida- ted. The results showed that the BP neural network combined with AHP was high-precision, fast, and objective, which could be used to food safety evaluation of circulation links of production, processing, and sales.