Journal
TEXTILE RESEARCH JOURNAL
Volume 77, Issue 8, Pages 565-571Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0040517507078061
Keywords
core-spun yarn; tensile properties; artificial neural network; multiple linear regression
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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-calidation technique was used to evaluate the performance of artificial neural network algorithm. Moreover the weight decay technique was also used for preventing the memorizaion.
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