Research on the Prediction of Tobacco Water Content Based on PCA-BP Neural Network
In order to realize the accurate prediction of the water content of the tobacco under the re-roasting machine,a tobacco water content prediction model based on principal component analysis and BP neural network was proposed. First,principal component analysis was used to extract the most characteristic factors of water content of re-roasted tobacco,and the feature matrix was obtained. Then,the feature matrix was input into BP neural network to construct a prediction model including the feature matrix and the water content of tobacco under re-roasting. The simulation results showed that the proposed model presented significant prediction ability in the prediction of water content of re-baking to-bacco,and the coefficient of determination reached 0.92. By using this method,we could assist in the optimization of the control parameters of tobacco re-baking and the improvement of the quality of re-baking tobacco.
Tobacco leavesMoisture contentPrincipal component analysisNeural networkPrediction model
万方数据
维普
