Simulated Neural Network Intelligent Computing Models for Predicting Shelf Life of Soft Cakes

Abstract

This paper highlights the potential of simulated neural networks for predicting shelf life of soft cakes stored at 30o C. Elman and self organizing simulated neural network models were developed. Moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input parameters and overall acceptability score was output parameter. Neurons in each hidden layers varied from 1 to 30. The network was trained with single as well as double hidden layers with 1500 epochs and transfer function for hidden layer was tangent sigmoid while for the output layer, it was pure linear function. The shelf life predicted by simulated neural network model was 20.57 days, whereas as actual shelf life was 21 days. From the study, it can be concluded that simulated neural networks are excellent tool in predicting shelf life of soft cakes.