Wavelength detection in FBG sensor networks using least squares support vector regression
A wavelength detection method for a wavelength division multiplexing (WDM) fiber Bragg grating (FBG) sensor network is proposed based on least squares support vector regression (LSSVR). As a kind of promising machine learning technique, LSSVR is employed to approximate the inverse function of t...
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| Main Authors: | , , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2014
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/105353 http://hdl.handle.net/10220/20496 http://dx.doi.org/10.1088/2040-8978/16/4/045402 |
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| 總結: | A wavelength detection method for a wavelength division multiplexing (WDM) fiber Bragg
grating (FBG) sensor network is proposed based on least squares support vector regression
(LSSVR).
As a kind of promising machine learning technique, LSSVR
is employed to
approximate the inverse function of the reflection spectrum. The LSSVR
detection model is
established from the training samples, and then the Bragg wavelength of each FBG can be
directly identified by inputting the measured spectrum into the welltrained
model. We also
discuss the impact of the sample size and the preprocess of the input spectrum on the
performance of the training effectiveness. The results demonstrate that our approach is
effective in improving the accuracy for sensor networks with a large number of FBGs. |
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