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
维普
万方数据
