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 :
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