Please use this identifier to cite or link to this item:
|Title:||Wavelength detection in FBG sensor networks using least squares support vector regression||Authors:||Chen, Jing
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics||Issue Date:||2014||Source:||Chen, J., Jiang, H., Liu, T., & Fu, X. (2014). Wavelength detection in FBG sensor networks using least squares support vector regression. Journal of Optics, 16(4), 045402-.||Series/Report no.:||Journal of optics||Abstract:||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.||URI:||https://hdl.handle.net/10356/105353
|DOI:||10.1088/2040-8978/16/4/045402||Rights:||© 2014 IOP Publishing Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Journal of Optics, IOP Publishing Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1088/2040-8978/16/4/045402].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.