Postprocessing of Near-Field Measurement Based on Neural Networks

This paper presents postprocessing based on neural network (NN) models to reconstruct the magnetic near-field profile with an improved spatial resolution for one or different frequencies. The models aim at decreasing the time required to perform near-field electromagnetic compatibility (EMC) measurements. The multilayer perceptron (MLP) NNs are used to determine the magnetic near field radiated by passive devices and power electronics components. An optimization method, called the split-sample method, is implemented to determine the structures of the NN. The results obtained with the proposed method are compared with the measurement results. A graphic interface (GUI) is created to simplify the utilization of the developed NN models.