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Neural Network Modeling of Physical Properties of Ground Wheat
Author(s) -
Fang Qi,
Biby Gerald,
Haque Ekramul,
Hanna Milford A.,
Spillman Charles K.
Publication year - 1998
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem.1998.75.2.251
Subject(s) - backpropagation , mean squared error , artificial neural network , biological system , root mean square , chemistry , coefficient of determination , statistics , approximation error , soil science , mathematics , environmental science , artificial intelligence , engineering , computer science , electrical engineering , biology
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.