Neural Networks vs Principal Component Regression for Prediction of Wheat Flour Loaf Volume in Baking Tests

摘要: Neural networks (NN) provide a simple means of predicting outcomes that depend upon complex, possibly nonlinear, relationships between many variables. A trained neural network was created and used to predict loaf volume breads made from different wheat cultivars. Although creating the NN required specialized skills considerable computational time, using “trained” estimate remix volume, very rapid only basic computer skills. Random Centroid Optimization (RCO) also employed choose best training parameters: learning rate = 0.820, smoothing factor 0.123, noise 0.056, number hidden neurons 5. more accurate, faster easier than Principal Component Regression Analysis.