基于Lambert-Beer理论与人工神经网络的混合荧光粉发射光谱预测
Yixing Cao (Fudan University)
Shanghuan Chen (Hohai University)
Yutong Li (Hohai University)
Yunjia Du (Hohai University)
Wei Chen (Changzhou Institute of Technology Research for Solid State Lighting)
Jiajie Fan (Fudan University, Changzhou Institute ofTechnology, TU Delft - Electronic Components, Technology and Materials)
Guo Qi Zhang (Fudan University, TU Delft - Electronic Components, Technology and Materials)
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Abstract
The emission spectra of high color rendering phosphors, mixed with the yttrium aluminium garnet, silicon based oxynitride and nitride based phosphors, were predicted by the Lambert-Beer theory and back propagation neural network (BP NN). Firstly, the modified Lambert-Beer model was used to calculate the proportional coefficient of the emission spectra of the mixed phosphors in ratios. Next, the BP NN was implemented to train and predict the proportional coefficients. Finally, the prediction of the emission spectra of the mixed phosphors was estimated and verified by the experimental measurements. The results show that the prediction error fraction of the proportional coefficients can be controlled within 5%; the predicted emission spectra by BP NN keep high agreement with the experimental measurements with lower RMSE and Δxy as 0.019 and 0.0016, respectively.