东华大学学报（英文版）2007，Vol.24Issue(6)：781-786,6.
Yarn Properties Prediction Based on Machine Learning Method
Yarn Properties Prediction Based on Machine Learning Method
摘要
Abstract
Although many works have been done to constructprediction models on yarn processing quality, the relationbetween spinning variables and yam properties has not beenestablished conclusively so far. Support vector machines(SVMs), based on statistical learning theory, are gainingapplications in the areas of machine learning and patternrecognition because of the high accuracy and goodgeneralization capability. This study briefly introduces theSVM regression algorithms, and presents the SVM basedsystem architecture for predicting yam properties. Model.selection which amounts to search in hyper-parameter spaceis performed for study of suitable parameters with grid-research method. Experimental results have been comparedwith those of artificial neural network(ANN) models. Theinvestigation indicates that in the small data sets and real-life production, SVM models are capable of remaining thestability of predictive accuracy, and more suitable for noisyand dynamic spinning process.关键词
machine learning/ support vector machines/artificial neural networks/ structure risk minimization/yarn quality predictionKey words
machine learning/ support vector machines/artificial neural networks/ structure risk minimization/yarn quality prediction分类
轻工纺织引用本文复制引用
YANG Jian-guo,L(U) Zhi-jun,LI Bei-zhi..Yarn Properties Prediction Based on Machine Learning Method[J].东华大学学报（英文版）,2007,24(6):781-786,6.基金项目
National Science Foundation and Technology Innovation Fund of P. R. China (No.70371040 and 02 LJ-14-05-01) （No.70371040 and 02 LJ-14-05-01）
