一种基于RBF神经网络的打印机光谱预测模型  

A Spectral Prediction Model of Printer Based on RBF Neural Network

作　　者：于海琦[1] 刘真[1] 田全慧[2] 

机构地区：[1]上海理工大学出版印刷与艺术设计学院,上海200093 [2]上海出版印刷高等专科学校,上海200093

出　　处：《影像科学与光化学》2015年第3期238-243,共6页Imaging Science and Photochemistry

基　　金：国家自然科学基金项目(41271446);上海市研究生创新基金项目(JWCXSL1402)资助

摘　　要：本文提出一种基于RBF(Radial Basis Function,径向基函数)神经网络的打印机光谱预测模型,通过扩展神经网络模型输入变量的项数提高模型的预测精度,扩展项多采用通道驱动值的交叉值、平方值。实验结果表明[1cmy]项的引入能够有效提高模型的预测精度,同时提高网络的泛化能力。而引入[cm2 cy2 mc2 my2 yc2 ym2]项会导致模型预测精度以及泛化能力降低。[1cmy]、[c2 m2y2]和[cm cy my]项的组合在预测精度和模型泛化能力上均是最优化的,对总样本预测的色度精度为0.475ΔE00,光谱精度RMSE为0.43%。因此选择[1cmy c2 m2y2 cm cy my c m y]作为输入变量的RBF神经网络训练模型是满足高精度光谱预测的最优模型。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.

关 键 词：RBF神经网络 光谱预测 打印机 扩展项 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]