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A neural network approach to real-time dielectric characterization of materials
Article first published online: 21 NOV 2002
DOI: 10.1002/mop.10639
Copyright © 2002 Wiley Periodicals, Inc.
1098-2760/asset/cover.gif?v=1&s=e392639464276853e6605df394388ece8e15fa91 "Microwave and Optical Technology Letters")
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How to Cite
Olmi, R., Pelosi, G., Riminesi, C. and Tedesco, M. (2002), A neural network approach to real-time dielectric characterization of materials. Microw. Opt. Technol. Lett., 35: 463–465. doi: 10.1002/mop.10639
Publication History
- Issue published online: 21 NOV 2002
- Article first published online: 21 NOV 2002
- Manuscript Received: 13 JUN 2002
- Abstract
- References
- Cited By
Keywords:
- artificial neural networks;
- complex permittivity;
- waveguide measurement
Abstract
Artificial neural networks (ANNs) are proposed as an inversion approach in microwave measurement methods of the dielectric characteristics of materials. Reflection and transmission methods require a proper electromagnetic (EM) model of the measurement system, and solutions in terms of the material permittivity are usually in implicit form, requiring an inversion procedure that can be costly in terms of computing time. In such applications, the ANN approach is favorable in that the EM computations are performed during the off-line training phase of the network. As an example of a real-world application, the ANN approach is tested on a waveguide method for the measurement of permittivity in the X band for which a working solution is yet available, showing that the parameters of interest (permittivity and thickness of the material under measurement) can be obtained in real-time with a negligible loss of accuracy. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 35: 463–465, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10639