粒子群优化
解调
超参数
核(代数)
光纤布拉格光栅
计算机科学
趋同(经济学)
极限学习机
理论(学习稳定性)
人工智能
算法
模式识别(心理学)
作者
Xin Xu,Yiping Wang,Dan Zhu,Jingzhan Shi
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-1
标识
DOI:10.1109/jsen.2022.3156595
摘要
A n o v e l machine learning based FBG sensing interrogation method is proposed in this paper. The method uses a Kernel Regularized Extreme Learning Machine optimized by Quantum Particle Swarm Optimization(QPSO-KRELM). The proposed method is divided into two steps. First, the prediction model is established through the KRELM. Second, the hyperparameters of the model are optimized by the usage of the QPSO algorithm. Due to the good stability and convergence of the QPSO-KRELM, the well-trained detection model can automatically and accurately extract the sensing information from the FBG spectrum. The experimental results demonstrate that the proposed method is reliable and efficient in interrogating the wavelength shift. Moreover, even if the FBG spectrum has few sample points, the method can still provide high demodulation accuracy.
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