Early warning modeling and analysis based on a deep radial basis function neural network integrating an analytic hierarchy process: A case study for food safety

Food safety is vital to the national economy and livelihood of people. Therefore, effective food safety warnings are helpful to the healthy and sustainable development of society. Focused on the early warning modeling for a certain scale of complex food safety inspection data, this paper proposes a novel early warning modeling method based on the deep radial basis function (DRBF) neural network that integrates an analytic hierarchy process (AHP). First, the AHP based on the entropy weight is used to obtain the risk fusion results of the inspection data as the expected output of the DRBF. Then, the DRBF model based on the autoencoder is used to build the early warning model, implementing feature learning to acquire the high-level representation of the food inspection data. Finally, the category data of sterilized milk from the food safety inspection data of a province in China is taken as a case study. Comparing the experimental results of the radial basis function (RBF) neural network, the back propagation (BP) neural network and the improved multilayer BP, the proposed DRBF model is found to have a better generalization ability and a better generalization effect for the complex food safety inspection data. Furthermore, the proposed early warning model is used to predict and analyze the risk of the inspection data from early September 2014. The results could be helpful for relevant departments to carry out early warning work and provide a scientific basis for guidance, thereby promoting the improvement of the food quality and reducing food risks.