Neuro-controller for broadcast lighting light-emitting diode to express white light with high color rendering index

Abstract

Technical characteristics analysis related to correlated color temperature (CCT), color rendering, and illuminance is required to use light-emitting diode (LED) as broadcast lighting. In general, to realize a white light source with a high color rendering index (CRI), we selected the appropriate emission intensity of RGBW LED through trial and error. However, the characteristics of the LED light source and environmental conditions make it difficult to perform the procedure several times. The objective of this study was to design a system that could control illuminance, CCT, and increment uv while having high CRI, as an LED control method for broadcasting lighting. The controller implements using a feed-forward neural network with excellent nonlinear function approximation capability. We measure data directly from the red green blue white (RGBW) LED system for neural network training. We then select data with high CRI from the measured raw data and choose data for neural network learning by removing measurement noise using the quadratic polynomial interpolation method. The performance evaluation confirms that the proposed neural network controller shows excellent results as an RGBW LED controller for broadcast lighting in the Planckian locus and all regions of white light.