Wavelength detection in spectrally overlapped FBG sensor network using extreme learning machine
This paper presents a novel learning-based method called extreme learning machine (ELM) to solve the Bragg wavelength detection problem in the fiber Bragg grating (FBG) sensor network. Based on building up a regression model, the proposed approach is divided into two phases: offline training ph...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/103790 http://hdl.handle.net/10220/24580 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This paper presents a novel learning-based method
called extreme learning machine (ELM) to solve the Bragg
wavelength detection problem in the fiber Bragg grating (FBG)
sensor network. Based on building up a regression model, the
proposed approach is divided into two phases: offline training
phase and online detection phase. Due to the good generalization
capability of ELM, the well-trained detection model can directly
and accurately determine the Bragg wavelengths of the sensors
even when the spectra of FBGs are completely overlapped. The
results demonstrate that the proposed method is efficient and
stable. It has shown competitive advantages in terms of the
detection accuracy, the offline training speed as well as the real
time detection efficiency. |
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