目的 实现打印机的光谱预测。方法 提出一种基于子空间划分的径向基函数（RBF）神经网络模型， 将打印机颜色空间划分成若干子空间， 在子空间中运用RBF神经网络， 对任意输入打印机驱动值根据其所在子空间实现其光谱值的预测。结果 该模型的预测精度较未进行子空间划分模型的有明显提高。结论 该模型能够满足高精度打印机光谱预测的要求。

The aim of this study was to establish a printer spectral prediction model using RBF (Radial Basis Function) neural network based on subspace partition. The color space of printer was divided into subspaces and RBF neural network models were applied in subspaces with least square method． Spectral reflectance of any printer motivation values were predicted by RBF neural network according to their own space. Experimental results showed that prediction accuracy of the model was obviously improved compared with models without subspace partition, which can satisfy the requirement of high-precision spectral prediction of printer.