Wavelength detection in FBG sensor networks using least squares support vector regression

• Main Authors: Chen, Jing, Jiang, Hao, Liu, Tundong, Fu, Xiaoli
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
• Online Access: https://hdl.handle.net/10356/105353; http://hdl.handle.net/10220/20496; http://dx.doi.org/10.1088/2040-8978/16/4/045402
• Institution: Nanyang Technological University

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.