Prediction of Optimum Gas Mixture for Highest SXR Intensity Emitted by A 4kj Plasma Focus Device Using Artificial Neural Network

  • Gholam Hossein Roshani Plasma and Fusion Research School, Nuclear Science and Technology Research Institute, Tehran, Iran
  • Morteza Habibi Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran
  • Asghar Sadighzadeh Plasma and Fusion Research School, Nuclear Science and Technology Research Institute, Tehran, Iran
  • Mohsen Afsharmanes Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran
  • Abbas Rezaei Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran
Keywords: Plasma focus, ANN, MLP, soft X-ray, highest intensity.

Abstract

In this study, artificial neural network (ANN) is investigated to predict the optimum gas mixture for highest soft X-ray (SXR) intensity emitted by a 4kJ plasma focus device. To do this multi-layer perceptron (MLP) neural network is used for developing the ANN model in MATLAB 7.0.4 software. In this model, the input parameters are voltage, Percentage of nitrogen in admixture and pressure and the output is SXR intensity. The obtained results show that the proposed ANN model has achieved good agreement with the experimental data and has a small error between the estimated and experimental values. Therefore, this model is a useful, reliable, fast and cheap tool to predict the optimum gas mixture for highest SXR intensity emitted by plasma focus devices.

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Published
2015-06-11
How to Cite
Roshani, G. H., Habibi, M., Sadighzadeh, A., Afsharmanes, M., & Rezaei, A. (2015). Prediction of Optimum Gas Mixture for Highest SXR Intensity Emitted by A 4kj Plasma Focus Device Using Artificial Neural Network. Boson Journal of Modern Physics, 1(1), 1-10. Retrieved from http://scitecresearch.com/journals/index.php/bjmp/article/view/228
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Vol 1 No 1: BJMP
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