基于Ga-BP神经网络的色纺纱配色

摘要

For the deficiencies of traditional color matching and color matching algorithm, BP neural network is used to pre-dict the formula of the colored spun yarn, and the genetic algorithm is introduced to improve the BP neural net-work. The results show that:BP neural network can be optimized when the genetic algorithm is introduced into it, but when the test sample is contained in the training sample data, the color matching accuracy of this Ga-BP neural network is very high and the mean formula absolute error is almost 0, while when the test sample is not in-cluded in the training samples, the color matching accuracy is lower and the mean formula absolute error is 0.033, the mean color difference of the first smaple is 1.69 CMC(2∶1), and more than 1 CMC(2∶1).%针对传统配色方法及配色算法存在不足之处，利用BP神经网络对色纺纱进行配方预测，并用遗传算法对其进行改进.结果表明：将遗传算法引入到BP神经网络，可优化BP神经网络配色模型；测试样本包含在训练样本中时，预测配方精度非常高，配方绝对误差均值几乎为0；而测试样本不包含在训练样本中时，预测配方精度较低，配方绝对误差均值为0.033，初次打样色差均值为1.69 CMC（2∶1），大于1 CMC（2∶1）.