## Predictors Generation by Partial Least Square Regression for microwave characterization of dielectric materials

Sadou, H. and Hacib, T. and Le Bihan, Y. and Meyer, O. and Acikgoz, H.

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##### Özet

In this paper, the microwave characterization of dielectric materials
using open-ended coaxial line probe is proposed. The measuring cell is a
coaxial waveguide terminated by a dielectric sample. The study consists
in extracting the real and imaginary part of the relative dielectric
permittivity (epsilon = epsilon'-j `') of the material under test from
the measurements of the probe admittance (Y-mes(f) = G(mes)(f) +
jB(mes)(f)) on a broad band frequency If between 1 MHz and 1.8 GHz),
hence a dirrect and inverse problems have to be solved. In order to build
a database, the direct problem is solved using Finite Elements Method
(FEM) for the probe admittance (Y(f) = G(f) + jB(f)). Concerning the
inverse problem, Partial Least Square (PLS) Regression (PLSR) is
investigated as a fast, simple and accurate inversion tool. It is a
dimensionality reduction method which aims to model the relationship
between the matrix of independent variables (predictors) X and the
matrix of dependant variables (response) Y. The purpose of PLS is to
find the Latent Variables (LV) having the higher ability of prediction
by projecting original predictors into a new space of reduced dimension.
The original inverse model has only three predictors (f, G and B) but is
nonlinear, so inspired by the extended X bloc method, more predictors
have been created mathematically from the original ones (for example:
1/f(2), B/f(2), GB, 1/B, G/f, f(2)G, fG(2)B, f(2)G(2)B(2), ... etc) in
order to take into account the nonlinearity, whence the appellation
Predictors Generation Partial Least Square Regression (PG-PLSR).
Inversion results of experimental measurements for liquid (ethanol,
water) and solid (PEEK (Polyether-ether-ketone)) samples have proved the
applicability and efficiency of PG-PLSR in microwave characterization.
Moreover, the comparison study in the last section has proved the
superiority of PG-PLSR on Multi-Layer Perceptron Neural Network (MTP-NN)
in terms of rapidity, simplicity and accuracy.... Daha fazla Daha az