Color prediction model of BP neural network based on multi-source feature fusion

Aiming at the limitation of fabric color characterization with a single data source, multi-source data feature fusion method was used to characterize fabric color information. The correlation coefficient and the error of BP neural network color matching model are used for quantitative analysis. The results show that for cotton and polyester fabrics, the correlation coefficients of multi-data source fusion features are greater than 0.9, which is higher than that of single data source, and the characterization ability is stronger. At the same time, this method can significantly reduce the model error and improve the precision. The average error is less than 0.03, and the accuracy of the model is improved by more than 9% compared with the input of a single data source, which proves the effectiveness and universality of the proposed method.