针对打印机的非线性以及印刷条件的复杂性，提出一种基于遗传算法(GA)优化的径向基(RBF)神经网络与子空间划分的打印机颜色预测模型。对打印机进行子空间划分，在子空间中进行模型的构建，采用GA 同时对RBF 神经网络的隐含层节点的中心和宽度参数进行优化进而构建了GA-RBF 神经网络模型。同时将本文算法与RBF 神经网络、Yule-Nielsen 修正的Cell Neugebauer(CYNSN)模型两种主流算法的预测精度进行了比较。实验结果表明，GA 的优化弥补了RBF 神经网络可调参数单一的缺陷，提高了模型的预测精度，与其他模型相比，该模型具有较高的预测精度和泛化能力，用于打印机的颜色预测是切实可行的。

A color prediction model of printer based on radial basis function (RBF) neural network optimized by genetic algorithm (GA) and subspace partition is presented to settle the nonlinear of printer and complexity of printing conditions. The color space of printer is divided into subspaces and the models are built in subspaces, GA-RBF neural network model is built by GA optimizing the hidden layer nodes and width parameters of RBF neural network. Prediction accuracy of the proposed algorithm is compared with RBF neural network and cellar Yule-Nielsen spectral neugebaue (CYNSN) model. Experimental results show that GA makes up for the defect of single adjustable parameter of RBF neural network and improves prediction accuracy. Compared with other models, the proposed model has high prediction accuracy and generalization ability. It is feasible for color prediction of printer.