NEURAL-NETWORK MODELING OF PHYSICAL-PROPERTIES OF GROUND WHEAT

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 w heat: geometric mean diameter (GMD), specific surface area increase (S SAI), 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 signifi cant reduction in root mean squared error (RMS), relative error (RE), and the increase in coefficient of determination R-2 (>0.98). The neur al network models are, therefore, capable of predicting the physical p roperties of the ground wheat.