Estimating Mean of Maximum Fields Inside Reverberation Chambers Using Deep Neural Networks

Recently, there has been great interest in estimating the mean of the maximum field strength in a nested reverberation chamber in such conditions that the field coupled inside the equipment under test (EUT) deviates from the parent distribution, thus generating a non-Rayleigh distribution. For this purpose, a new regression model with the deep feedforward neural network is proposed to predict the mean of the maximum field inside a nested reverberation chamber configuration. In our proposed method, a frequency range that comprises the EUT in the overmoded regime is treated as an input of the network, and the mean of the maximum field is treated as the output of the network. Several networks with different numbers of hidden layers are trained, while adaptive learning rates and early stopping techniques are used to improve the network training process, subsequently reducing the uncertainties. After training, the networks are verified using a test set that is not implicitly employed during the training process. The testing and training mean-squared errors (625e-5 and 325e-5) with the network with five layers have a good agreement for a considered configuration, demonstrating a novel regression model that is able to rigorously extrapolate the mean of the maximum field in the other frequency steps that are not used in the training set.