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Electromagnetic measurements are widely used to gain information about an object through interaction of electromagnetic fields with the physical properties of this object. The inversion problem is the process of estimating object parameters from electromagnetic records. This problem has a nonlinear nature and can be formulated as an optimization scheme. In this paper, we introduce an inversion methodology to estimate the electrical properties of a two-dimensional inhomogeneous layered scattering object. The proposed methodology deals with the inversion problem as a learning process through two multilayer perceptron artificial neural network designs. Several neural network design parameters were tuned to achieve the best inversion performance. Moreover, the proposed neural networks were tested against noise presence in terms of error criteria and proved to be effective in solving the inverse scattering problem.

Estimation of Electromagnetic Properties for 2D Inhomogeneous Media Using Neural Networks