Prediction of Materials Density according to Number of Scattered Gamma Photons Using Optimum Artificial Neural Network

Through the study of scattered gamma beam intensity, material density could be obtained. Most important factor in this densitometry method is determining a relation between recorded intensity by detector and target material density. Such situation needs many experiments over materials with different...

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Bibliographic Details
Published in:Journal of computational methods in physics 2014-06, Vol.2014, p.1-6
Main Authors: Roshani, Gholam Hossein, Feghhi, Seyed Amir Hossein, Shama, Farzin, Salehizadeh, Abolfazl, Nazemi, Ehsan
Format: Article
Language:English
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Summary:Through the study of scattered gamma beam intensity, material density could be obtained. Most important factor in this densitometry method is determining a relation between recorded intensity by detector and target material density. Such situation needs many experiments over materials with different densities. In this paper, using two different artificial neural networks, intensity of scattered gamma is obtained for whole densities. Mean relative error percentage for test data using best method is 1.27% that shows good agreement between the proposed artificial neural network model and experimental results.
ISSN:2356-7287
2314-6834
DOI:10.1155/2014/305345