Yarn Quality Prediction for Small Samples Based on AdaBoost Algorithm

• Published in: 东华大学学报（英文版） Vol. 40; no. 3; pp. 261 - 266
• Main Authors: LIU Zhiyu, CHEN Nanliang, WANG Jun
• Format: Journal Article
• Language: English
• Published: College of Textiles,Donghua University,Shanghai 201620,China%College of Textiles,Donghua University,Shanghai 201620,China 01.06.2023; Key Laboratory of Textile Science & Technology,Ministry of Education,Donghua University,Shanghai 201620,China
• Subjects: quality prediction; yarn; AdaBoost algorithm; generalization ability; small sample
• ISSN: 1672-5220
• DOI: 10.19884/j.1672-5220.202302005

TS101.9; In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model)was established.A prediction model based on a linear regression algorithm(LR model)and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model)were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76％and 87％higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.