目的 为了实现打印机的光谱预测， 提出一种GA-BP神经网络结合子空间划分的预测模型。方法将打印机颜色空间划分成若干子空间， 在子空间中运用GA-BP神经网络， 对任意输入打印机的驱动值，根据其所在子空间实现光谱值的预测； 采用主成分分析对光谱反射率进行降维， 在简化了神经网络结构的同时， 保持了对检测样本较高的识别精度。结果 模型预测精度较未进行子空间划分时有了明显提高。结论 提出的模型能够满足高精度打印机光谱预测的要求。

A spectral prediction model of printer based on GA-BP neural network and subspace partition was proposed in this paper. Color space of printer was divided into subspaces and GA-BP neural network models were applied in subspaces．Spectral reflectance of any printer motivation values can be predicted by GA-BP neural network according to their own subspace. The principal component analysis was used for dimensionality reduction of spectral reflectance, which simplified the neural network structure and maintained the high identification accuracy for the test samples at the same time. Experimental results showed that prediction accuracy of the model improved obviously than the model without subspace partition, which can satisfy the requirement of high-precision spectral prediction of printer.