Equivalent Electromagnetic Hybrid Dipole Based on Cascade-Forward Neural Network to Predict Near-Field Magnitude of Complex Environmental Radiation

This article proposes a new method to predict the radiation field in a complex environment. The magnetic field probe is used to scan the magnitude of the radiation field, and a cascade-forward neural network (CFNN) is introduced to establish the nonlinear relationship between Greens function and the magnitude of the radiation field. The trained CFNN can replace the real radiation source in a complex environment and produce the same radiation field, which is helpful to analyze electromagnetic interference problems. The innovation of this article is to establish an equivalent source based on only scanning the magnitude of the radiation field. At the same time, the electric current source and the magnetic current source of the radiator are equivalent to electric dipoles and magnetic dipoles, respectively. After CFNN training, we can predict the radiation field beyond the scanning plane. Simulation and measurement examples are used to verify the effectiveness of the proposed method.