Mutual complement between statistical and neural network approaches for rock magnetism data analysis
Expert Systems with Applications: An International Journal
Anti-germ Performance Prediction for Detergents Based on Elman Network on Small Data Sets
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Expert Systems with Applications: An International Journal
A novel application of neural networks for instant iron-ore grade estimation
Expert Systems with Applications: An International Journal
Neural network prediction of ascorbic acid degradation in green asparagus during thermal treatments
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
A novel method for shelf life prediction was established for a packaged moisture sensitive snack. Artificial neural network (ANN) based on multilayer perceptrons (MLP) with back propagation algorithm was developed to predict the shelf life of packaged rice snack stored at 30^oC and 75% RH, 30^oC and 85% RH and 40^oC and 75% RH, comparable to tropical storage conditions. The MLP predicted shelf lives were then compared to the actual shelf lives. Using MLP algorithm, many factors could be incorporated into the model including food characteristics, package properties, and storage environments. The MLP neural network comprised an input layer, one hidden layer and an output layer. The network was trained using Lavenberg-Marquardt (LM) algorithm. The performance of a MLP neural network was measured using regression coefficient (R^2) and mean squared error (MSE). The MLP algorithm gave R^2 of 0.98, and MSE of 0.12. MLP offers several advantages over conventional digital computations, including faster speed of information processing, learning ability, fault tolerance, and multi-output ability.