Cereal Chemistry · 1998 · 24 citations · 7 references
EngineeringAgricultural EngineeringMachine LearningSoil MechanicsSoil PhysicsNeural ModelingNeural NetworksSoil Physical PropertiesBackpropagationGeometric Mean DiameterIndependent VariablesInput Data AnalysisNeural Network ModelingNeural Network PredictionPhysical PropertiesSpecific Surface Area
ABSTRACT Physical properties of ground materials from roller mills are affected by the characteristics of wheat and the operational parameters of the roller mill. Backpropagation neural networks were designed, trained, and tested for the prediction of three physical properties of ground wheat: geometric mean diameter (GMD), specific surface area increase (SSAI), and break release (BR). Eight independent variables were used as input data. Compared to conventional statistical models, the accuracy of prediction was improved substantially, as reflected by the significant reduction in root mean squared error (RMS), relative error (RE), and the increase in coefficient of determination R 2 (>0.98). The neural network models are, therefore, capable of predicting the physical properties of the ground wheat.
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