Research Of Reasoning Model Of Control Knowledge Of Food Safety Based On Fuzzy Bayesian Network

Bayesian network is an effective tool for uncertainty reasoning, but in practice, we often meet fuzzy stochastic uncertain reasoning problems. For example we want to forecast the possibility of tomorrow as "good weather" ,future weather "state" is a random event, but "the weather is good state" is a fuzzy event. How to use Bayesian network to solve this kind of mixed uncertainty knowledge inference has become a research hotspot. This paper proposes a novel approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm : first of all, according to the principle of fuzzy mathematics, define of a mixed event and its probability, define the conditions fuzzy probability table for the first time, effectively solves the problem of the denote of the random variable, find the parameters of the Gaussian membership by clustering, optimize structure learning and parameter learning by Genetic Algorithm, find the optimal network structure according to the classification error and membership error of reasoning, and modify network parameters by modifying the parameters of membership functions, in particular the parameterαin the definition of the fuzzy probability is optimized to determine, in the end set up a fuzzy Bayesian network.View of the current food safety control is difficult to advance the field of risk diagnosis, early warning, scientific definition of responsibility after the real problems. After research the data of food safety control features in this field, this paper proposes reasoning model of control knowledge of food safety based on Fuzzy Bayesian Network: after the research of the data of Guangzhou Municipal Quality Supervision Bureau of the traceability system, extract the indicators related to food safety risk, define the value of this indicators by statistical method, obtain the sample data, and use the approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm to establish reasoning model of control knowledge of food safety. The application results shows that the approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm, increases the computational complexity and running time because of its use of fuzzy logic, but just using fuzzy logic can directly reflect the possibility of high food safe risk in the food production process in the reasoning and diagnosis. Compared with the general Bayesian network, fuzzy Bayesian network has a higher accuracy of inference.