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

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。

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.