Near Field to Far Field Transformation Using Neural Networks and Source Reconstruction

Abstract

Neural networks are proposed as an efficient tool able to perform near field to far field transformation, directly or through the application of the Theorem of Equivalence. A neural network can be trained to relate near field data with far field data or with an equivalent current distribution and then this one with the corresponding far field radiation pattern. Significant numerical examples are presented.