Open AccessAn Improved Temperature Compensation Method for Fiber Bragg Grating Pressure Sensor Based on Extreme Learning Machine
Author(s) -
Hongying Guo,
Chen Jiang,
Zhumei Tian,
Aizhen Wang
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.118
Subject(s) - compensation (psychology) , fiber bragg grating , grating , nonlinear system , wavelength , extreme learning machine , materials science , optics , acoustics , computer science , optoelectronics , artificial intelligence , physics , artificial neural network , psychology , quantum mechanics , psychoanalysis
According to the problem of the sensor nonlinear changes occur at high temperatures, extreme learning machine model, is presented in this thesis the pressure sensitive grating and removing the temperature of the grating experiment data for training, establish a nonlinear model of wavelength, temperature, predict the experimental temperature, then the temperature data of pressure-sensitive grating the training set of training samples, the nonlinear model, temperature - wavelength prediction test set sample output wavelength, achieve the goal of improved temperature compensation method. The experimental results show that the algorithm can achieve a more ideal temperature compensation effect.