Abstract
In this paper, chemical properties of wheat dough are predicted using different soft computing tools. Here, back-propagation and genetic algorithm techniques are used to predict the parameters and a comparative study is made. Wheat grains are stored at controlled environmental conditions. The content of fat, moisture, ash, time and temperature are considered as inputs whereas protein and carbohydrate contents are chosen as outputs. The prediction algorithm is developed using back-propagation algorithm, number of layers are optimized and mean square errors are minimized. The errors are further reduced by optimizing the weights using Genetic Algorithm and again the outputs are obtained. The error between predicted and actual outputs is calculated. It has been observed that with back-propagation along GA model algorithm, errors are less compared to the simple back-propagation algorithm. Hence, the given network can be considered as beneficial as it predicts more accurately. Numerical results along with discussions are presented.
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Kaur, M., Guha, P., Mishra, S. (2016). Intelligent Prediction of Properties of Wheat Grains Using Soft Computing Algorithms. In: Choudhary, R., Mandal, J., Auluck, N., Nagarajaram, H. (eds) Advanced Computing and Communication Technologies. Advances in Intelligent Systems and Computing, vol 452. Springer, Singapore. https://doi.org/10.1007/978-981-10-1023-1_8
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DOI: https://doi.org/10.1007/978-981-10-1023-1_8
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