A Novel Halftone Dot Prediction Model Based on BP Neural Network

机译：基于BP神经网络的新型网点预测模型。

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摘要

The purpose of this study is to reduce the ready time of offset printing by establishing a halftone dot prediction model (HDPM) based on BP neural network. The input data of HDPM is spectral reflectance data and the output data is dot area percentage of monochrome inks. To make sure the Mean Square Error (MSE) between the trained output data and the original data negligible, some experiments are performed. We obtained a set of results for dot area percentage of monochrome inks. The HDPM has stable performance, but it doesn't work well under some special conditions, such as ink with black and dark tone. To improve the precision of HDPM, we increase the training samples of the corresponding area. Therefore, the prediction performance of HDPM has improved, and it does increase the efficiency of printing production in some levels.

机译：本研究的目的是通过建立基于BP神经网络的半色调网点预测模型（HDPM）来减少胶印的准备时间。 HDPM的输入数据是光谱反射率数据，输出数据是单色墨水的点面积百分比。为了确保训练后的输出数据和原始数据之间的均方误差（MSE）可以忽略不计，请执行一些实验。我们获得了单色墨水点面积百分比的一组结果。 HDPM具有稳定的性能，但是在某些特殊条件下，例如带有黑色和深色调的墨水，效果不佳。为了提高HDPM的精度，我们增加了相应区域的训练样本。因此，HDPM的预测性能得到了改善，并且确实在一定程度上提高了印刷生产的效率。

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来源
《Advanced sciencesin graphic communications and packaging》|2017年|61-67|共7页
会议地点 Beijing(CN)
作者
Mengying Liu; Yuanlin Zheng; Ziwei Tang; Wei Wang;
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作者单位

Faculty of Printing, Packing Engineering and Digital Media Technology, Xi'an University of Technology, Xi'an, China;

Faculty of Printing, Packing Engineering and Digital Media Technology, Xi'an University of Technology, Xi'an, China,Shaanxi Provincial Key Laboratory of Printing and Packaging Engineering, Xi'an, China;

Faculty of Printing, Packing Engineering and Digital Media Technology, Xi'an University of Technology, Xi'an, China;

Faculty of Printing, Packing Engineering and Digital Media Technology, Xi'an University of Technology, Xi'an, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
BP neural network; Halftone dot printing; Color matching;

机译：BP神经网络；半色调网点印刷；颜色匹配;

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A Novel Halftone Dot Prediction Model Based on BP Neural Network

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