dc.contributor.authorJiang, Hao
dc.contributor.authorChen, Jing
dc.contributor.authorLiu, Tundong
dc.date.accessioned2015-01-12T03:28:41Z
dc.date.available2015-01-12T03:28:41Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.citationJiang, 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.en_US
dc.identifier.urihttp://hdl.handle.net/10220/24580
dc.description.abstractThis 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.en_US
dc.format.extent4 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE photonics technology lettersen_US
dc.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].en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
dc.titleWavelength detection in spectrally overlapped FBG sensor network using extreme learning machineen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/LPT.2014.2345062
dc.description.versionAccepted versionen_US


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