关键词: RBF神经网络, 光谱预测, 打印机, 扩展项

Abstract:

A spectral prediction model of printer based on RBF (Radial Basis Function) neural network was proposed in this paper. Prediction accuracy of model is improved by extending the input variables of neural network. The extensions included cross value or square value of channel driven value. Experimental results show that introduction of [1 cmy] item can effectively improve the prediction precision and the generalization ability, introduction of [cm² cy² mc² my² yc² ym²] item can decrease the prediction precision and generalization ability of the network. Introduction of combination of [cmy]、[c²m²y²] and [cm cy my] item can achieve the optimized prediction accuracy and generalization ability. This combination terms can reach the colorimetric accuracy of 0.475 ΔE₀₀ and spectral accuracy of 0.43% RMSE of all samples. Thus, RBF neural network model with input variables of [1 cmy c²m²y² cm cy my c m y] is the most optimized model that meets spectral prediction with high resolution.

Key words: RBF neural network, spectral prediction, printer, extensions