基于全光衍射深度神经网络的矿物拉曼光谱识别方法

提出了一种基于全光衍射神经网络的矿物拉曼光谱识别方法。首先，分析矿物拉曼光谱的数据结构特征，对比分析了传统神经网络与光学衍射神经网络的异同，根据预处理后的数据构建光学衍射神经网络；然后，采用交叉熵损失函数和Adam算法对光学衍射神经网络进行训练，得到优化的网络参数；最后，在仿真条件下，验证和分析不同栅格高度精度对矿物识别正确率的影响，给出了不同栅格高度精度对应的网络正确率及正确率损失。该方法在RRUFF矿物拉曼光谱数据库上的测试结果显示:五类矿物识别正确率为94.2%，证明利用光学衍射神经网络进行拉曼光谱分类具有可行性，为光学衍射神经网络的应用提供参考；栅格高度在6 bit精度条件下，五类矿物正确率为93.6%，证明栅格高度离散化能够在保证网络正确率的同时极大降低光栅制作难度，为光栅制备提供理论支撑。

Raman mineral recognition method based on all-optical diffraction deep neural network

A recognition method of mineral Raman spectrum based on all-optical diffraction neural network was proposed. Firstly, the data structure characteristics of the Raman spectra of minerals were analyzed, the similarities and differences between traditional neural network and optical diffractive neural network were compared and analyzed, and the optical diffractive neural network was constructed according to the preprocessed data. Secondly, the cross entropy loss function and Adam algorithm were used to train the optical diffractive neural network, and the optimized network parameters were obtained. Finally, under the simulation conditions, the effects of different grid-height accuracy on the accuracy of mineral recognition were verified and analyzed, and the network accuracy and accuracy loss corresponding to the different grid-height accuracy was given. The test results on the RRUFF mineral Raman spectrum database show that the recognition accuracy of five kinds of minerals is 94.2%, which proves the feasibility of Raman spectrum recognition using optical diffractive neural network. It provides a reference for the application of optical diffractive neural network; the accuracy of five kinds of minerals under the condition of 6 bit grid-height resolution is 93.6%, which proves that grid height discretization can not only ensure the accuracy of network, but also greatly reduce the difficulty of grating fabrication. It provides theoretical support for grating fabrication.