苎麻纺纱各工序性能指标的预测

Prediction on Yarn Performance in Ramie Spinning Process by BP Neural Network

文章运用灰色关联分析法分析了苎麻纺纱过程中前道工序的性能指标对下道工序性能指标的影响,并结合BP神经网络建立了梳麻、精梳、末道并条、二道粗纱、细纱各工序中半成品或成品的性能指标的预测模型,各模型的平均预测误差均低于10%,这表明这些模型用于预测是可行的。

The grey relational analysis was applied to analyze the effect of performances in former process to the next process,then combined with BP neural network to set up the forecast models of the products＇ performances in the carding,combing,finishing drawing,roving and spinning processes.The mean relative error between the predicted results and measured values was less than 10%,which shows that these models were available in practice.