Abstract

This study proposes artificial neural networks (ANN) to predict the breakdown voltages in N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> + SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> gas mixtures. The proposed ANN consists of one input layer, two hidden layers and one output layer, which is essentially the predicted breakdown voltage. In order to train the ANN, the experimental data available in literature for N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> + SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> have been used. When compared with the experimental data the average relative errors on predicted breakdown voltages are found to be less than plusmn5% for training as well as for testing in all cases using the proposed ANNs. Since the average errors are less than 5%, it is recommended to use the proposed ANNs to predict the breakdown voltages.

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