Application of Bayesian Network in Moisture Control System of Ordering Machine

In the moisture control system of ordering machine, the irregularity of tobacco input and its moisture content and the uncertainty of the ordering process make it impossible to control the moisture content in ordered tobacco leaf precisely with the functional relations between variables.Bayesian network had the advantages of expressing and processing the dependence relations between stochastic variables, the dependence relation between the moisture content in ordered leaf and control variables is qualitatively described by means of the Bayesian network structure established via the analysis of ordering machine.The degree of dependence between variables could be quantitated by the Bayesian network parameter learning algorithm with the realtime data collected during tobacco processing, and the controlled variable was estimable from measurable variables.The practical data indicated that by using Bayesian network method, the process capability index(Cpk) increased from 0.371 to 2.799, the fluctuation of moisture content in ordered leaf reduced obviously, and the standard error decreased from 1.512 to 0.236.