Influence of loss function on training the LSTM network in wall moisture tomography

Abstract

This study concerns research on using electrical impedance tomography (EIT) to image moisture inside the porous walls of buildings. In order to transform the electrical measurements into the values of the reconstructed 3D images, a neural network containing the LSTM layer was used. The objective of the study was to evaluate the impact of various loss functions on the efficacy of a neural network’s learning process. During the training process, three distinct variations of the loss function were employed, namely mean squared error (MSE), Huber, and a hybrid of MSE + Huber, to attain the desired outcome. Given that the primary focus of the study was on the loss function, the particular neural network architecture employed was deemed non-essential. In order to minimize the influence of the neural network architecture on the outcomes of the test, a comparatively uncomplicated neural model was implemented, comprising a solitary LSTM layer and a single fully connected layer.