Determination of the response of Ar + SF6 to crossed electric and magnetic fields using an artificial neural network

Document Type

Conference Proceeding

Publication Date


Publication Title

IEEE Xplore

Conference Name

2007 Annual Report Conference on Electrical Insulation and Dielectric Phenomena


In this study, an artificial neural network (ANN) is proposed to predict the mean energy and deflection angle that cause a breakdown in Ar+SF6 mixtures under crossed electric and magnetic fields. The selected ANN structure for this study is a fully connected hierarchical network consisting of an input layer, a hidden layer and an output layer. To train the ANN, results from a Monte-Carlo simulation have been used. The activation function for neurons is a sigmoid function with 0.5 threshold value. The predictions have R2-values equal to 0.998 for epsiv and 0.9998 for thetas. The relative error between the results of the Monte Carlo simulation and the predicted values of mean energy and deflection angle using the ANN is found to be less than 10%.


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