邢倍,周世生,罗如柏.基于 LMBP 神经网络的柔印专色配色模型研究[J].包装工程,2014,35(3):88-92.
XING Bei,ZHOU Shi-sheng,LUO Ru-bai.Flexography Color Matching Model Based on the LMBP Neural Network[J].Packaging Engineering,2014,35(3):88-92. |
| 基于 LMBP 神经网络的柔印专色配色模型研究 |
| Flexography Color Matching Model Based on the LMBP Neural Network |
| 投稿时间：2013-10-18 修订日期：2014-01-01 |
| DOI： |
| 中文关键词: BP 人工神经网络 LMBP 算法 柔版印刷 专色配色模型 精度检验 |
| 英文关键词: BP Neural Network LMBP algorithm flexography special color matching model model precision inspection |
| 基金项目:陕西省“13115冶 科技创新工程项目基金(2009ZJDC-06) |
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| 中文摘要: |
| 目的 针对柔印专色配色模型甚少问题,构建柔版印刷专色配色模型。 方法 以 BP 人工神经网络非线性、自学习等特点为基础,引入 LMBP 算法对传统的 BP 算法进行改进,从而构建柔版印刷专色配色模型,同时运用 Matlab 软件结合标准印刷测试版对模型进行训练。 结果 隐层节点为 17 的单隐层 BP 算法虽然也可达到预计要求,但隐层节点为 8 的单隐层 LMBP 算法精度更高,逼近效果更好。结论 该柔版印刷专色配色模型符合精度要求,可以用于实践。 |
| 英文摘要: |
| Objective To solve the shortage problem of flexography special color matching model. Methods On the basis of the nonlinear and self-learning characteristics in the BP Neural Network, this paper brought in the LMBP algorithm to improve the traditional BP algorithm and build the flexography special color matching model. At the same time, combining with the printing betas, we trained the model with Matlab software. Results Based on the analysis of training results, we concluded that although the BP algorithm with 17 notes in hidden layer could meet the expected requirements, the LMBP algorithm with 8 notes in hidden layer had higher precision and better approximation effect. Conclusion This model met the accuracy requirement and can be used in practice. |
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