Using Artificial Neural Networks Approach for the Color Enhance of High Power LEDs

Hsi-Chao Chen; Guo-Yang Wu; Chi-Hao Yang; Peng-Ying Chen; Mei-Jyun Lai; Kuo-Ting Huang

doi:10.1117/12.929533

15 October 2012 Using Artificial Neural Networks Approach for the Color Enhance of High Power LEDs

Hsi-Chao Chen, Guo-Yang Wu, Chi-Hao Yang, Peng-Ying Chen, Mei-Jyun Lai, Kuo-Ting Huang

Proceedings Volume 8484, Twelfth International Conference on Solid State Lighting and Fourth International Conference on White LEDs and Solid State Lighting; 84841G (2012) https://doi.org/10.1117/12.929533
Event: SPIE Optical Engineering + Applications, 2012, San Diego, California, United States

Abstract

High power light-emitting diodes (HP-LEDs) always are applied for energy-saving to replace the traditional light sources. HP-LEDs lighting has been regarded in the next generation lighting. In this study, the RGY colors enhance of whit LED lighting was researched and modulated by artificial neural network (ANN). An ANN model was used to investigate the correlated color temperature (CCT) and luminous flux (Lux) for the white LED enhanced with different power of single RYG LEDs. The starting color temperature of the white LED will be set at 7500K (D75 white light standard), then changed the voltage of the single LED of the red, green or yellow, respectively, to find the best tuning function for the color temperature and luminous efficiency. These results exhibited that changing the voltage of red LED had the broader color temperature from 7500 K to 1500 K than the range of green and yellow LEDs from 7500K to 8200K and 7500K to 4700K, respectively. Then, these experimental results were used as input data for the training model. After the learning model was completed, an analysis was used to obtain the internal representation of the color information by the responses of the individual chips of the three hidden units in the middle layer. Identification rate of data would be achieved to 100% by the neural network pattern-recognition tool. Anyway, the correlation coefficient could reach to 99% by the ANN fitting tool for the color enhancement.

Citation Download Citation

Hsi-Chao Chen, Guo-Yang Wu, Chi-Hao Yang, Peng-Ying Chen, Mei-Jyun Lai, and Kuo-Ting Huang "Using Artificial Neural Networks Approach for the Color Enhance of High Power LEDs", Proc. SPIE 8484, Twelfth International Conference on Solid State Lighting and Fourth International Conference on White LEDs and Solid State Lighting, 84841G (15 October 2012); https://doi.org/10.1117/12.929533

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