  An inverse method for parameters estimation of infinite cylinders (the
dielectric properties, location, and radius) in two dimensions from
amplitude-only microwave information is presented. To this end two different
Artificial Neural Networks (ANN) topologies are compared; Multilayer Perceptron
(MLP) and a Convolutional Neural Network (CNN). Several simulations employing
the Finite Differences in Time Domain (FDTD) method are performed to solve the
direct electromagnetic problem and generate training, validation, and test sets
for the ANN models. The magnitude of the mean errors in estimating the position
and size of the cylinder are up to (1.9 $\pm$ 3.3) mm and (0.2 $\pm$ 0.8) mm
for the MLP and CNN, respectively. The magnitude of the mean percentage
relative errors in estimating the dielectric properties of the cylinder are up
to (6.5 $\pm$ 13.8) % and (0.0 $\pm$ 7.2) % for the MLP and CNN, respectively.
The errors in the parameters estimation from the MLP model are low, however,
significantly lower errors were obtained with the CNN model. A validation
example is shown using a simulation in three dimensions. Measurement examples
with homogeneous and heterogeneous cylinders are presented aiming to prove the
feasibility of the described method.
