An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming
Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a s...
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| Vydáno v: | Journal of engineered fibers and fabrics Ročník 7; číslo 2 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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London, England
SAGE Publications
01.06.2012
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| ISSN: | 1558-9250, 1558-9250 |
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| Abstract | Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, Gene Expression Programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R2-Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R2-value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm. |
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| AbstractList | Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, Gene Expression Programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R2-Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R2-value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm. Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, Gene Expression Programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R 2 -Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R 2 -value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm. |
| Author | Fallahpour, Alireza Shanbeh, Mohsen Moghassem, Abdolrasool |
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| Cites_doi | 10.1080/00405000701679632 10.1108/RJTA-14-01-2010-B001 10.1080/00405009508658772 10.1177/0040517506067331 10.1177/004051757504500409 10.1177/0040517508097792 10.1177/004051759706700203 10.1177/004051756703700807 10.1177/004051750407400902 10.1080/00405000903080837 10.1533/joti.2005.0135 10.1108/RJTA-11-04-2007-B008 10.1080/00405000802131178 10.1533/joti.2004.0073 10.1016/j.ins.2007.06.029 10.1177/004051750407400806 10.1016/j.eswa.2008.07.017 10.1177/004051750007001001 10.1177/004051759706700109 |
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| Keywords | Rotor spun yarn Draw frame Breaking strength Production speed Break draft Artificial neural network Gene expression programming |
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| Title | An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming |
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