Neural networks for microwave characterization of material samples in rectangular cavities

In order to characterize material samples of different sizes at microwaves, use was made of a rectangular metallic cavity, which is partially filled by these material samples, having arbitrary locations. Subsequently, by using a multi-layer perceptron (MLP) network, its dielectric constant, dielectr...

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Bibliographic Details
Main Authors: Penirshke, A., Freese, J., Schubler, M., Jakoby, R.
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Dielectric constant
Dielectric loss measurement
Dielectric losses
Dielectric materials
Dielectric measurements
Inorganic materials
Loss measurement
Multilayer perceptrons
Neural networks
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Summary:In order to characterize material samples of different sizes at microwaves, use was made of a rectangular metallic cavity, which is partially filled by these material samples, having arbitrary locations. Subsequently, by using a multi-layer perceptron (MLP) network, its dielectric constant, dielectric losses and its amount could be accurately extracted from measurements of the magnitude of the scattering parameter |S/sub 11/| only, since the phase information was not available. The input for this network is generated by a proper preprocessing of the simulated and measured magnitude of the return loss |S/sub 11/|. The investigations indicate very good agreement of the simulated and measured data, thus, a simulation-based training of neural networks and an subsequent parameter extraction of the material samples from |S/sub 11/|-measurements was possible with high accuracy.
DOI:10.1109/ISSPIT.2003.1341194