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23 May 2005 Radial-basis function network for the approximation of quasi-distributed FBG sensor spectra with distorted peaks
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Proceedings Volume 5855, 17th International Conference on Optical Fibre Sensors; (2005) https://doi.org/10.1117/12.623814
Event: 17th International Conference on Optical Fibre Sensors, 2005, Bruges, Belgium
Abstract
This paper describes the use of a neural network, specifically a Radial-Basis function network, to approximate spectra of the signal reflected by a fibre Bragg grating sensor. This approximation will help the interpretation of the data acquired from the sensor when it is supposed to work in a quasi-distributed way, but its reflected spectrum has non-uniformities which would cause the misinterpretation of the data if quasi-distributed demodulation techniques are used. Results using an emulated double-peaked spectrum from a fibre Bragg grating sensor show that the common practice of fitting with a gaussian curve and then finding its peak or directly finding the maximum of the raw spectrum would cause a larger error if compared to finding the peak of an approximated spectrum using the RBF network.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aleksander S. Paterno, Lucia Valeria R. Arruda, and Hypolito Jose Kalinowski "Radial-basis function network for the approximation of quasi-distributed FBG sensor spectra with distorted peaks", Proc. SPIE 5855, 17th International Conference on Optical Fibre Sensors, (23 May 2005); https://doi.org/10.1117/12.623814
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