The spectral power distribution prediction of LED light source based on Gaussian mathematical model and improved residual network.

Accurate prediction of the spectral power distribution of light-emitting diodes in multi-phosphor systems is challenging because of the influence of material composition and operating conditions. This paper proposes a spectral prediction framework that combines a Gaussian mathematical model with an improved residual neural network. First, light-emitting diode samples were fabricated using red and green phosphors, and their spectral power distributions were measured. The Gaussian model was then employed to extract the characteristic parameters from the continuous spectral power distributions, and these parameters were used to construct the corresponding dataset. Based on this dataset, a neural network framework was established to map the phosphor mixing ratio, phosphor-to-silicone ratio, and drive current to the Gaussian parameters. Through systematic comparative and extended validation experiments, it is demonstrated that the coefficient of determination for spectral power distribution reconstructed by the Gaussian mathematical model exceeds 0.99. The proposed improved residual network significantly outperforms baseline residual network and recent state-of-the-art methods, achieving superior predictive accuracy and stability. Furthermore, ablation studies validate the effectiveness of the attention mechanism, while sensitivity analyses and independent dataset evaluations further confirm the robustness and generalization capability of the proposed framework. The proposed model significantly enhances spectral prediction accuracy in multi-phosphor systems and achieves rapid mapping from material composition and electrical parameters to the resulting spectrum. A new modeling framework for customized light-emitting diode spectral design is provided in this study, and theoretical support is offered for the intelligent optimization of healthy-lighting and high-color-rendering light sources.