Abstract The reflection spectrum characteristics of fiber Bragg grating are very important for its sensing applications. A method of “Feature Extraction-Support Vector Machine (FE-SVM)” to identify spectral types is developed and experimentally demonstrated. The reflection spectrum characteristics of fiber Bragg grating are analyzed and extracted based on theory and simulation calculation. The characteristic data were preprocessed, and the distorted spectrum type recognition model was optimized. Training the data through the network, the recognition accuracy of Support Vector Machine (SVM) network for 1000 groups of FBG mixed spectrum reached 99.9%. To verify the recognition effect of reflection spectrum features, a time-varying temperature field was established as the non-uniform field. The accuracy rate reached 96.875%. The proposed FE-SVM method is characterized by fast response, high reliability and easy optimization, which has a promising application in environmental parameter measurement and substance classification.

中文翻译：

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基于支持向量机的非均匀场下FBG反射谱识别与分类

摘要 光纤布拉格光栅的反射光谱特性对其传感应用非常重要。开发了一种识别光谱类型的“特征提取支持向量机（FE-SVM）”方法并进行了实验证明。基于理论和仿真计算，对光纤布拉格光栅的反射光谱特性进行了分析和提取。对特征数据进行预处理，优化畸变谱类型识别模型。通过网络训练数据，支持向量机（SVM）网络对1000组FBG混合频谱的识别准确率达到99.9%。为验证反射光谱特征的识别效果，建立时变温度场作为非均匀场。准确率达到96.875%。