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

This paper highlights the potential of simulated neural networks for predicting shelf life of soft cakes stored at 10 o 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