Evaluation of electromagnetic immunity of layered structures by neural networks

In this study, exploitation of artificial neural networks for an efficient solution of a simple electromagnetic compatibility problem is discussed. Two parallel dielectric layers are penetrated by a perpendicular electromagnetic wave. A standing wave is formed between layers. Radial basis function networks are employed to estimate the electric field intensity between the layers for both the harmonic wave and the pulse wave illumination. The electrical parameters of dielectric layers can influence the field distribution inside the investigated structure. Probabilistic neural networks are used to classify parameters of layers related to critical intensities of internal fields. Classification abilities of probabilistic networks are compared with a conventional k-NN method both for a dense training set and a sparse one.