电测与仪表2025，Vol.62Issue(2)：62-68,7.DOI:10.19753/j.issn1001-1390.2025.02.008
基于广义回归神经网络的光纤光栅传感器解调技术研究
Research on demodulation technology of fiber Bragg grating sensor based on generalized regression neural network
摘要
Abstract
Aiming at the problems of large error and poor stability of traditional wavelength demodulation of fiber Bragg grating sensor,a wavelength peak detection method of fiber Bragg grating sensor based on generalized regres-sion neural network and improved particle swarm optimization algorithm is proposed.Through the improved particle swarm optimization algorithm,the smoothing factor of the generalized regression neural network is optimized to im-prove the accuracy of the central wavelength calculation of the generalized regression neural network.The perform-ance of the proposed method at different central wavelengths is analyzed through experiments.The results show that the proposed method is more stable than the traditional method,and the demodulation error is smaller,the absolute deviation of the overall central wavelength is reduced by 35.90％and 24.24％,and the relative wavelength varia-tion deviation is reduced by 20.00％and 13.04％.关键词
光纤光栅/峰值检测/中心波长/粒子群优化算法/广义回归神经网络Key words
fiber Bragg grating/peak detection/central wavelength/particle swarm optimization algorithm/gener-alized regression neural network分类
信息技术与安全科学引用本文复制引用
夏翔,李贤良,潘华,闫东,张晓锋,张云辉..基于广义回归神经网络的光纤光栅传感器解调技术研究[J].电测与仪表,2025,62(2):62-68,7.基金项目
国家重点研发计划项目(2017YFB0903100) （2017YFB0903100）
国网浙江省电力公司丽水供电公司研发项目(5211LS220003) （5211LS220003）
