NEURAL NETWORK APPROACH TO SHAPE RECONSTRUCTION OF A CONDUCTING CYLINDER

In this study, the shape reconstruction of a conducting cylinder by making use of electromagnetic scattered fields is presented. The problem is treated with the Method of Moment (MoM) and Feed Forward Neural Networks (FF-NN) is applied. The shape of the cylinder is represented by a Fourier series while applying MoM, a matrix equation is obtained whose elements are expressed numerically by using discrete points. The scattered field data and Fourier coefficients of the cylinder are used for training the NN as inputs and outputs respectively. The NN results are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed. The effect of the noise on the scattering field is also investigated.