Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103790
Title: Wavelength detection in spectrally overlapped FBG sensor network using extreme learning machine
Authors: Jiang, Hao
Chen, Jing
Liu, Tundong
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
Issue Date: 2014
Source: Jiang, H., Chen, J., & Liu, T. (2014). Wavelength detection in spectrally overlapped FBG sensor network using extreme learning machine. IEEE photonics technology letters, 26(20), 2031-2034.
Series/Report no.: IEEE photonics technology letters
Abstract: 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.
URI: https://hdl.handle.net/10356/103790
http://hdl.handle.net/10220/24580
DOI: 10.1109/LPT.2014.2345062
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LPT.2014.2345062].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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