Neural Network Modeling for Near-field Power Control Problems

Abstract

In this paper, neural network modeling techniques are used to model and solve the inverse problem of the Near-Field Power Pattern Control. The near-field intensities as the input to the inverse model, whereas the source phases are the output. The effect of the size of the training data set on the accuracy of the results is investigated. Extensive numerical tests indicate that the results predicted by the proposed models are in excellent agreement with the theoretical data obtained from the existing analytical solutions of the forward problem.