DocumentCode
3429152
Title
Prediction of fiber diameter of spunbonding nonwovens by using neural network and multiple regression models
Author
Bo, Zhao
Author_Institution
Coll. of Textiles, Zhongyuan Univ. of Technol., Zhengzhou, China
Volume
2
fYear
2010
fDate
25-27 June 2010
Abstract
In this article, the multiple regression model and neural network model are designed and used to predicting the fiber diameter of spunbonding nonwovens from the process parameters. The neural network has good approximation capability and fast convergence rate, and it can provide quantitative predictions of fiber diameter. The results show the ANN model can yield more accurate and stable predictions than the multiple regression model. The predicted and experimental values agree well, indicating that the neural network is an excellent method for predictors.
Keywords
fabrics; neural nets; prediction theory; production engineering computing; regression analysis; ANN model; convergence rate; fiber diameter prediction; multiple regression model; neural network; process parameter; spunbonding nonwoven; Artificial neural networks; Computer networks; Equations; Neural networks; Neurons; Polymers; Predictive models; Temperature; Textile fibers; Throughput; artificial neural network mode; fiber diameter; multiple regression model; spunbonding nonwoven;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Design and Applications (ICCDA), 2010 International Conference on
Conference_Location
Qinhuangdao
Print_ISBN
978-1-4244-7164-5
Electronic_ISBN
978-1-4244-7164-5
Type
conf
DOI
10.1109/ICCDA.2010.5541361
Filename
5541361
Link To Document