Multi-color prediction model based on BP-NN optimized by GA and PCA

作者：Yu Hai Qi; Liu Zhen^*; Tian Quan Hui

来源：Chinese Journal of Luminescence, 2015, 36(6): 711-717.

DOI：10.3788/fgxb20153606.0711

• BP neural networks
• Color prediction model
• Dimensionality reduction
• Multi-color reproductions
• Prediction accuracy
• Prediction precision
• Spectral reflectances
• Transformation model

Color partition method is the main method of multi-color reproduction. In this paper, the whole color area of 7 primary colors was divided into 6 partitions in group of 3 primary colors, and principal component analysis (PCA) was used to dimensionality reduction of the color spectrum reflectance. A three layers BP neural network was established to describe the transformation model of dot area coverage and spectral reflectance. Genetic algorithm (GA) was adopted for optimizing the weight threshold of BP NN to improve the prediction accuracy of model. Experimental results show that GA-BP model has higher prediction precision and stability compared with BP ANN and cell neugebauer model. When the number of training sample is 64 and test sample is 216 in the partition, the optimized model can predict color with the accuracy of 1.669 mean ΔEab*and 0.7% spectral RMSE. By comparing with the non-optimized model with the training sample numbers of 125, 216, 343 (mean ΔEab*are 3.267, 2.776, 2.175 and spectral RMSE are 0.97%, 0.79%, 0.76%), the prediction accuracy of GA-BP model with the training sample numbers of 64 is equal to the accuracy of the non-optimized model with the training sample number of 343. The results show that GA-BP model can reach high precision color reproduction with small amount of samples, and have good portability in practice.

• 出版日期2015