Prediction of Materials Density according to Number of Scattered Gamma Photons Using Optimum Artificial Neural Network. (10th June 2014)

• Record Type: Journal Article
• Authors: Roshani, Gholam Hossein; Feghhi, Seyed Amir Hossein; Shama, Farzin; Salehizadeh, Abolfazl; Nazemi, Ehsan
• Other Names: Incerti Sebastien Academic Editor.
• Is Part Of: Journal of computational methods in physics. Volume 2014(2014)
• Publication Date: 2014-06-10
• Subjects: Physics -- Data processing -- Periodicals; Mathematical physics -- Data processing -- Periodicals; Mathematical physics -- Data processing; Physics -- Data processing; Electronic journals; Periodicals; Electronic journals; 530.0285
• DOI: 10.1155/2014/305345
• Languages: English
• ISSNs: 2356-7287

Abstract

Abstract : 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.