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

Journal article (2021)

Authors

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

J. Fan Electronic Components, Technology and Materials - , Fudan University, Changzhou Institute ofTechnology

Kouchi Zhang Fudan University, Electronic Components, Technology and Materials -

Research Group

Electronic Components, Technology and Materials () (TU Delft)

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

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http://resolver.tudelft.nl/uuid:64c75f55-51b1-4eb4-903b-21bbed442385

Published Date

2021

Language

Chinese (Traditional)

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Electronic Components, Technology and Materials

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.

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