基于BP神经网络的纺纱质量预报模型

简述了纺纱质量预报的理论与实际意义，应用了BP神经网络的标准算法。通过建立BP神经网络模型对细纱条干不匀率(CV）断裂强力(BS)与纺纱断头率(ED)进行预报，并与多元线性回归方法进行比较，验证了BP神经网络预报的准确性与高效性。最后以纺纱断头率(ED)预报为例，指出了今后神经网络在纺纱质量预报应用中亟待解决的问题。

Yarn Property Prediction Models Based on BP Neural Network

The theoretic and practical significance of yarn property prediction has been introduced. The relevant algorithm of BP neural network has been applied in this experiment. Three models to predict the yarn unevenness value (CV), breaking strength (BS) and ends down in spinning (ED) have been established and compared with the multivariate linear regression method. The experimental results show that the prediction models based on BP neural network are more precise and efficient than those of regression analysis. Meanwhile, it is necessary to do further research on the accurate prediction of ends down in spinning.