DocumentCode :
1895371
Title :
Using Artificial Neural Network Model on Studying Fiber Diameter of Spunbonding Nonwovens: Comparison with Mathematical Empirical Statistical Method Model
Author :
Bo, Zhao
Author_Institution :
Coll. of Textiles, Zhongyuan Univ. of Technol., Zhengzhou, China
Volume :
1
fYear :
2009
fDate :
10-11 Oct. 2009
Firstpage :
423
Lastpage :
426
Abstract :
In this article, the mathematical statistical model and artificial neural network model are established and used to predict the fiber diameter of spunbonding nonwovens. The artificial neural network model has good approximation capability and fast convergence rate, and is employed in this research. The results show it can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the mathematical statistical model, which shows that the artificial neural network technique is really an effective and viable modeling method when the required number of experimental data sets is available. The effects of process parameters on fiber diameter are also determined by the ANN model. By analyzing the results of the mathematical statistical model, the effects of process parameters on fiber diameter can be predicted.
Keywords :
neural nets; statistical analysis; textile fibres; textile industry; ANN model; artificial neural network model; convergence rate; experimental data sets; fiber diameter; mathematical empirical statistical method model; process parameters; spunbonding nonwovens; Artificial neural networks; Mathematical model; Neural networks; Neurons; Optical fiber testing; Polymers; Predictive models; Statistical analysis; Textile fibers; Textile technology; artificial neural network model; fiber diameter; mathematical empirical statistical method model; spunbonding nonwoven;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
Type :
conf
DOI :
10.1109/ICICTA.2009.110
Filename :
5287620
Link To Document :
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