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

Authors: Gholam Hossein Roshani; Morteza Habibi; Asghar Sadighzadeh; Mohsen Afsharmanesh; Abbas Rezaei;

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

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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Keywords

Plasma Focus; ANN; MLP; Soft X-Ray; Highest Intensity.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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