目的 研究 LCD显示器的光谱特征化。方法 提出一种基于RBF神经网络的显示器光谱特征化模型； 扩展神经网络模型输入变量的项数， 以提高特征化模型的精度。结果 实验结果表明：［rg rb gb］ 项的引入， 提高了特征化模型的光谱和色度精度， 以及网络的泛化能力； 引入 ［r2 g2 b2］，［r2 g2 b2］，［rg2 rb2gr2 gb2 br2 bg2］ 均会导致模型精度下降及泛化能力降低； 以 ［r g b rg rb gb］ 作为神经网络输入变量的特征化模型， 在精度和泛化能力上均是最优化的， 实现了平均色差为0.14 的色度精度。结论 选择扩展项［rg rb gb］ 作为输入变量的RBF神经网络模型对LCD显示器进行光谱特征化， 是一种高精度显示器特征化的最优模型。

The aim of this work was to study the spectral characterization of LCD. A spectral characterization model based on RBF neural network was proposed in this paper. The prediction accuracy of model was improved by extending the input variables of neural network. Experimental results showed that introduction of［rg rb gb］item could effectively improve the characterization chromaticity and spectral precision of the model as well as the generalization ability of the network, while introduction of［r2 g2 b2］，［r2 g2 b2］，［rg2 rb2 gr2 gb2 br2 bg2］item could decrease both the characterization precision of the model and the generalization ability of the network. The characterization model with input variable of［r g b rg rb gb］terms of RBF neural network achieved the optimal precision and generalization ability, reaching the colorimetric accuracy of 0.14. Thus, RBF neural network model with input variables of［rg rb gb］was the most optimized model for spectral characterization of LCD with high resolution.