Prediction of automotive radiated emission using machine learning

A machine learning model that predicts automotive radiated emissions under CISPR25 conditions from near-field noise is developed as a neural network. The model demonstrates a versatile performance that predicts radiated emissions from samples other than those used in the learning phase. It shows low dependency of predictive performance on the neural network’s hyperparameters.