Predicting the Physiological Characteristic Changes in Pears Subjected To External Loads Using Artificial Neural Network (Ann)-Part 1: Static Loading

This research was aimed to study the effects of loading force and storage period on the physiological characteristic of pears. in this experiment, the pears were subjected to quasistatic loading (wideedge and thinedge) and different storage periods (5, 10 and 15 days). the amounts of the fruits’ total phenol, antioxidant and vitamin c contents were evaluated after each storage period. in the present study, multilayer perceptron (mlp) artificial neural network featuring a hidden layer and two activating functions (hyperbolic tangentsigmoid) and a total number of 5 and 10 neurons in each layer were selected for the loading force and storage period so that the amounts of the total phenol, antioxidants and vitamin c contents of the fruits could be forecasted. according to the obtained results, the highest r2 rates for thinedge and wideedge loading in a network with 10 neurons in the hidden layer and a sigmoid activation function were obtained for total phenol content( =0.9539 =0.9865), antioxidant ( =0.9839 =0.9649) and vitamin c ( =0.9758); as for wideedge loading in a network with 5 neurons in the hidden layer and hyperbolic tangent activation function, the highest r2 rate of vitamin c content was obtained equal to =0.9865. according to the obtained results, the neural network with these two activation functions possesses an appropriate ability in overlapping and predicting the simulated data based on real data.

Predicting the physiological characteristic changes in pears subjected to external loads using Artificial Neural Network (ANN)-Part 1: Static loading

This research was aimed to study the effects of loading force and storage period on the physiological characteristic of pears. In this experiment, the pears were subjected to quasistatic loading (wideedge and thinedge) and different storage periods (5, 10 and 15 days). The amounts of the fruits’ total phenol, antioxidant and vitamin C contents were evaluated after each storage period. In the present study, multilayer perceptron (MLP) artificial neural network featuring a hidden layer and two activating functions (hyperbolic tangentsigmoid) and a total number of 5 and 10 neurons in each layer were selected for the loading force and storage period so that the amounts of the total phenol, antioxidants and vitamin C contents of the fruits could be forecasted. According to the obtained results, the highest R2 rates for thinedge and wideedge loading in a network with 10 neurons in the hidden layer and a sigmoid activation function were obtained for total phenol content( =0.9539 =0.9865), antioxidant ( =0.9839 =0.9649) and vitamin C ( =0.9758); as for wideedge loading in a network with 5 neurons in the hidden layer and hyperbolic tangent activation function, the highest R2 rate of vitamin C content was obtained equal to =0.9865. According to the obtained results, the neural network with these two activation functions possesses an appropriate ability in overlapping and predicting the simulated data based on real data.