Analysis of two modeling methodologies for predicting the tensile properties of cotton-covered nylon core yarns

• Published in: Textile research journal Vol. 77; no. 8; pp. 565 - 571
• Main Authors: Gharehaghaji, A.A, Shanbeh, M, Palhang, M
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.08.2007; Sage Publications Ltd
• Subjects: Algorithms; Artificial neural networks; Breaking; Errors; linear models; lint cotton; Mathematical models; Neural networks; nylon; Nylons; Regression; Regression analysis; simulation models; Studies; Tensile properties; Tensile strength; textile fibers; Yarn; Yarns; artificial neural network; tensile properties; core-spun yarn; multiple linear regression
• ISSN: 0040-5175; 1746-7748
• DOI: 10.1177/0040517507078061

In this study, the capability of artificial neural networks and multiple linear regression methods for modeling the tensile properties of cotton-covered nylon core yarns based on process parameters were investigated. The developed models were assessed by verifying Mean Square Error (MSE) and Correlation Coefficient (R-value) the test data prediction. The results indicated that artificial neural network algorithm has better performance in comparison with multiple linear regression. The difference between the mean square error of predicting these two models for breaking strength and breaking elongation was 0.365 and 0.119, respectively. The five-fold cross-validation technique was used to evaluate the performance of artificial neural network algorithm. Moreover, the weight decay technique was also used for preventing the memorization.