An Equivalent Radiation Source Reconstruction Method Based on Enhanced Artificial Neural Network

In this article, an enhanced artificial neural network (ANN) is proposed for equivalent radiation source reconstruction in the electromagnetic interference (EMI) analysis. The half-space Green's function of the dipole array is taken as the input and the radiation field obtained by near-field scanning is taken as the output of the enhanced ANN. The loss function of the enhanced ANN consists of the magnitude loss and the gradient loss. Compared with the conventional ANN only using the magnitude loss, this article introduces the gradient information of the radiation field into the loss function in the enhanced ANN training, which can accurately analyze EMI problems with higher efficiency. Numerical and measurement examples show that the proposed method can accurately reconstruct the radiation source with the error less than 5%, and the number of the scanning points in the enhanced ANN is reduced to 60% compared with that in the conventional ANN.