Abstract
The full-spectrum white light-emitting diode (LED) emits light with a broad wavelength range by mixing all lights from multiple LED chips and phosphors. Thus, it has great potentials to be used in healthy lighting, high resolution displays, plant lighting with higher color rendering index close to sunlight and higher color fidelity index. The spectral power distribution (SPD) of light source, representing its light quality, is always dynamically controlled by complex electrical and thermal loadings when the light source operates under usage conditions. Therefore, a dynamic prediction of SPD for the full-spectrum white LED has become a hot but challenging research topic in the high quality lighting design and application. This paper proposes a dynamic SPD prediction method for the full-spectrum white LED by integrating the SPD decomposition approach with the artificial neural network (ANN) based machine learning method. Firstly, the continuous SPDs of a full-spectrum white LED driven by an electrical-thermal loading matrix are discretized by the multi-peak fitting with Gaussian model as the relevant spectral characteristic parameters. Then, the Back Propagation (BP) and Genetic Algorithm-Back Propagation (GA-BP) NNs are proposed to predict the spectral characteristic parameters of LEDs operated under any usage conditions. Finally, the dynamically predicted spectral characteristic parameters are used to reconstruct the SPDs. The results show that: (1) The spectral characteristic parameters obtained by fitting with the Gaussian model can be used to represent the emission lights from multiple chips and phosphors in a full-spectrum white LED; (2) The prediction errors of both BP NN and GA-BP NN can be controlled at low level, that is to say, our proposed method can achieve a highly accurate SPD dynamic prediction for the full-spectrum white LED when it operates under different operation mission profiles.
Highlights
With the improvement of living standards, people’s requirements on lighting have gradually shifted from environmental protection and energy saving to the pursuit of health and comfort
The results show that: (1) The spectral characteristic parameters obtained by fitting with the Gaussian model can be used to represent the emission lights from multiple chips and phosphors in a full-spectrum white light-emitting diode (LED); (2) The prediction errors of both Back Propagation (BP) NN and Genetic Algorithm-Back Propagation (GA-BP) NN can be controlled at low level, that is to say, our proposed method can achieve a highly accurate spectral power distribution (SPD) dynamic prediction for the full-spectrum white LED when it operates under different operation mission profiles
The SPD decomposition modeling with the Gaussian function fitting is present in Fig. 6, which includes the original SPD, four extracted individual spectrum, and cumulative peak-fitting model, respectively
Summary
With the improvement of living standards, people’s requirements on lighting have gradually shifted from environmental protection and energy saving to the pursuit of health and comfort. Fan [12] et al used the Gaussian and Lorentz models to extract SPD features, and achieved a dynamic and accurate predicting of the color coordinates, correlation color temperature (CCTs), CRIs and estimating the residual life of phosphor converted white LED (PC-wLED). As one of most popular supervised learning methods, Artificial Neural Network (ANN) has been proven as an effective way on data mining and processing. It abstracts the human brain neuron network from the perspective of information processing, establishes a simple model, and forms different networks according to different connection modes [17].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
