In this book some applications of artificial neural network in nuclear engineering are presented. In densitometry, number of scattered and counted gamma photons highly depends on material density. Using this relation, two different multi-layer perceptron artificial neural networks are proposed to predict material density. The results of proposed ANNs show that the presented model could be employed in densitometry of materials. Furthermore, the development of an ANN model for prediction of the highest value of X-ray yield in PFs is showed. The comparison between predicted and experimental results by ANN model illustrates that there is a good adaptation between them. So, the MLP architecture can be applied as a high efficient tool to predict the highest value of X-ray yield in the PFs.
Some Applications of Artificial Neural Network in Nuclear Engineering
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In this book some applications of artificial neural network in nuclear engineering are presented. In densitometry, number of scattered and counted gamma photons highly depends on material density. Using this relation, two different multi-layer perceptron artificial neural networks are proposed to predict material density. The results of proposed ANNs show that the presented model could be employed in densitometry of materials. Furthermore, the development of an ANN model for prediction of the highest value of X-ray yield in PFs is showed. The comparison between predicted and experimental results by ANN model illustrates that there is a good adaptation between them. So, the MLP architecture can be applied as a high efficient tool to predict the highest value of X-ray yield in the PFs.
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