摘要
概括分析精毛纺粗纱过程特点和BP神经网络建模特性。基于粗纱工艺参数之间相互关联的特点,提出利用主成分分析法对精毛纺厂采集到的粗纱生产数据进行预处理。得到纤维特性、毛条质量不匀、毛条牵伸状态、毛条含杂和毛条并合情况这5个综合指标,消除了原输入变量之间的不独立性。主成分分析后的数据输入BP网络建模分析表明:输入层和隐层节点数减少,网络结构大为简化,网络的学习速率和性能提高;粗纱CV值和粗纱单重20组数据预报相对误差率由之前的4.86%和3.35%分别降低到2.24%和1.95%,网络精度进一步提高。预报值和实测值之间的相关性分析也表明相关系数有显著提高。
The characteristics of worsted roving procedure and BP neural network have been analyzed.Based on interdependence of all parameters,the principal component analysis（PCA） has been proposed to preprocess the roving procedure′s data collected from a worsted mill.The five general indexes： fiber characteristic,top weight unevenness,top drafting state,top impurities content and top drawing state have been gained,thus eliminating the original variables′interdependence.After PCA the data were inputted to BP network for modeling.The results indicate that the number of node decreased;the network structure simplified;the performance and learning rate of network enhanced.The relative mean error percent between the forecast values ofCVand weight of 20 groups of test samples of roving and the measured values are 2.24% and 1.95% compared to 4.86% and 3.35% before PCA respectively;the precision is improved.The correlation coefficient between the predict value and measured value is also distinctly enhanced.
出处
《纺织学报》
CAS
CSCD
北大核心
2008年第9期34-37,50,共5页
Journal of Textile Research
关键词
主成分分析
BP神经网络
精毛纺
粗纱
principal component analysis
BP neural network
worsted
roving
