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

Translated title of the contribution: Predication of Emission Spectra for Mixed Phosphors Using Lambert-Beer Theory and Artificial Neural Network

Yixing Cao, Shanghuan Chen, Yutong Li, Yunjia Du, Wei Chen, Jiajie Fan*, Guoqi Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Translated title of the contributionPredication of Emission Spectra for Mixed Phosphors Using Lambert-Beer Theory and Artificial Neural Network
Original languageChinese (Traditional)
Pages (from-to)2393-2398
Number of pages6
JournalXiyou Jinshu Cailiao Yu Gongcheng/Rare Metal Materials and Engineering
Volume50
Issue number7
Publication statusPublished - 2021

Keywords

  • Artificial neural network
  • Emission spectrum
  • High color rendering LED
  • Lambert-Beer theory
  • Mixed phosphors

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