The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

2010 ◽  
Vol 33 ◽  
pp. 74-78
Author(s):  
B. Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Statistical Regression ◽  
Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

Studying on the Fiber Diameter of Polypropylene (PP) Spunbonding Fabric by Means of Artificial Neural Network Model and Physical Model

2010 ◽  
Vol 426-427 ◽  
pp. 356-360
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Good Prediction ◽  
Artificial Neural

In this work, the artificial neural network model and physical model are established and utilized for predicting the fiber diameter of polypropylene(PP) spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the physical model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

Applying Artificial Neural Network Model on Investigating the Fiber Diameter of Polybutylene Terephthalate (PBT) Spunbonding Nonwovens: Comparison with Mathematical Statistical Method

2010 ◽  
Vol 426-427 ◽  
pp. 692-696
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Method ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Computer Assisted ◽  
Polybutylene Terephthalate ◽  
Artificial Neural

In this paper, two models are founded and introduced to predict the fiber diameter of polybutylene terephthalate spunbonding nonwovens from the spunbonding process parameters. The results indicate the artificial neural network model has good approximation capability and fast convergence rate, and it can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the mathematical statistical method. This area of research has great potential in the field of computer assisted design in spunbonding technology.

scholarly journals Predicting the Fiber diameter of Spunbonding Nonwovens Via Empirical Statistical methods and Neural Network Model

2013 ◽  
Vol 14 (1) ◽  
pp. 5323-5328
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Fiber Diameter ◽  
Ann Model ◽  
Network Methods ◽  
Artificial Neural ◽  
Approximation Capability

In this paper, the empirical statistical and artificial neural network methods are established. We present a comparative study of two modeling methodological for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The radial basis neural network, which has good approximation capability and fast convergence rate, is employed in this work, and it can provide quantitative predictions of fiber diameter. The effects of process parameters on fiber diameter are also determined by the ANN model. The results show the artificial neural network model yield more accurate and stable predictions than the statistical method, which reveals that artificial neural network technique is really an effective and viable modeling method.

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