BP neural network optimization of Stearns-Noechel model for color matching of wool color spinning yarns
In order to improve the accuracy of color matching of wool color spinning yarns,the relationship between hue,brightness,saturation in color characteristics,and the parameter M value of the Stearns-Noechel model was investigated by mathematical and statistical methods.The parameter M value of the Stearns-Noechel model was optimized by using the backpropagation(BP)neural network,and compared with the traditional methods such as the optimal average M value and the wavelength-optimized M value.The results show that the optimization of color matching methods of the Stearns-Noechel model using the BP neural network has some improvement in color prediction accuracy than the other two traditional optimization methods.Among 99 samples of wool color-blended yarns,the BP neural network optimization method obtained the smallest average color difference of 1.177 3.In addition,54%of these samples had a color difference of less than 1.This indicates that the use of combining color features and the optimization of parameters of the Stearns-Noechel model using the BP neural network has good results and significantly improves the accuracy of color prediction accuracy of wool color-blended yarns.
colored spun yarnStearns-Noechel modelbackpropagation neural networkcolor predictioncolor features
NETL
NSTL
万方数据
维普
