Spectrally tunable light source based on deep neural network model.

• Based on the measured dataset, we propose a tunable light source using a deep neural network (DNN) spectral matching algorithm. • Improvement of matching accuracy by using neural networks to solve the instability problem of modulation and the nonlinearity problem during spectral matching. • Build a communication link between algorithms and PWM regulation to optimize the speed of spectral matching of light sources. A spectrally tunable light source based on deep neural network (DNN) model is proposed in this work, which can reproduce arbitrary spectra accurately and rapidly. After calculating the scale factors using the trained DNN model, the target spectrum can be reproduced by regulating the combined monochromatic LEDs based on the pulse width modulation (PWM) signal with corresponding duty cycle. The standard solar spectrum and measured natural spectra at different times are reproduced using the proposed spectrally tunable light source. It is demonstrated that there is a good agreement between the target spectra and the corresponding reproduced spectra, with a fitted correlation index of above 0.9. Furthermore, the proposed spectrally tunable light source is able to transform spectrum at a frequency higher than 25 Hz and occupies only 750 KB memory space. As a result, the proposed spectrally tunable light source system can offer a high accuracy, fast speed and low cost approach to the development of specially light sources. [ABSTRACT FROM AUTHOR]