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- Title
基于支持向量机的精纺毛织物透气性预测.
- Authors
邵景峰; 王希尧
- Abstract
To quickly and efficiently predict the air permeability of the worsted fabrics, the relationship between the worsted fabrics′ parameters and the air permeability was deeply analyzed. After compared and analyzed the existing forecasting methods, and took use of the advantages of fast training and less parameter selection with SVM, a regression model of worsted fabrics′ air permeability based on support vector machine was presented in this paper, and the relationship between the 34 kinds of worsted fabrics′ parameters and the air permeability was analyzed. In the 34 groups of samples, 27 groups were randomly selected as training sets, and 7 groups were used as test sets. Under the condition of C = 1 325.525 8 and σ =0.102 8, the experiment achieved good results, and the average prediction accuracy was as low as 4%. Compared with the BP regression model, the SVM model reduced the average experimental error by 3%. This paper demonstrated that the SVM model yields more accurate prediction of worsted fabrics′ air permeability than the BP model.
- Subjects
SUPPORT vector machines; REGRESSION analysis; AIR bases; PERMEABILITY; ARTIFICIAL membranes
- Publication
Wool Textile Journal, 2019, Vol 47, Issue 8, p66
- ISSN
1003-1456
- Publication type
Article
- DOI
10.19333/j.mfkj.2018100280807