Table of Contents
Journal of Computational Methods in Physics
Volume 2014 (2014), Article ID 305345, 6 pages
http://dx.doi.org/10.1155/2014/305345
Research Article

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

1Radiation Application Department, Shahid Beheshti University, G.C., Tehran, Iran
2Electrical Engineering Department, Razi University, Kermanshah, Iran
3Electrical Engineering Department, Islamic Azad University of Kermanshah, Kermanshah, Iran
4Nuclear Engineering Department, Amirkabir University of Technology, Tehran, Iran

Received 26 January 2014; Revised 22 April 2014; Accepted 22 April 2014; Published 10 June 2014

Academic Editor: Sebastien Incerti

Copyright © 2014 Gholam Hossein Roshani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.