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Radial-basis function network for the approximation of FBG sensor spectra with distorted peaks

机译:径向基函数网络用于峰失真的FBG传感器光谱的近似

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The implementation of a radial-basis function network to approximate spectra of the signal reflected by a fibre Bragg grating sensor is reported. This algorithm helps the interpretation of the data acquired with equipment as an optical spectrum analyser. Results using a double-peaked spectrum from a uniform fibre Bragg grating sensor show that the common practice of fitting the spectrum with different interpolation methods and finding its peak, or directly finding the maximum intensity position of the raw spectrum, would cause a larger error when compared to searching for the peak of an approximated spectrum using the proposed neural network. An example is demonstrated through two experiments measuring the volumetric shrinkage of polymeric resin using a uniform FBG and a HiBi FBG embedded in the material. The obtained accuracy is higher than that obtained with the simple non-processed peak detection.
机译:报告了径向基函数网络的实现,以近似由光纤布拉格光栅传感器反射的信号的频谱。该算法有助于将设备获得的数据解释为光谱分析仪。使用来自均匀光纤布拉格光栅传感器的双峰光谱的结果表明,使用不同的插值方法拟合光谱并找到其峰值,或直接找到原始光谱的最大强度位置的常用做法会在以下情况下产生较大的误差:与使用拟议的神经网络搜索近似光谱的峰值相比。通过两个实验,使用均匀的FBG和嵌入材料中的HiBi FBG测量聚合树脂的体积收缩率,这是一个示例。所获得的准确度高于简单的未处理峰检测所获得的准确度。

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