基于神经网络的食品安全评价模型构建研究

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.

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

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.