A COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION MODELS AS IN PREDICTORS OF FABRIC WEFT DEFECTS

被引：0
|
作者：
Kargi, V. Sinem Arikan [1 ]
机构：
[1] Uludag Univ, Econ & Adm Sci Fac, Econometr Dept, Bursa, Turkey
来源：
TEKSTIL VE KONFEKSIYON | 2014年 / 24卷 / 03期
关键词：
Artificial neural network; Multilayer perceptron model; Multiple linear regression model; Fabric weft defect; Prediction;
D O I：
暂无
中图分类号：
TB3 [工程材料学]; TS1 [纺织工业、染整工业];
学科分类号：
0805 ; 080502 ; 0821 ;
摘要：
Predicting uncertainty is quite important for the reliability of decisions to be made by business managers. Contemporary problems are complex, and their solutions require scientific decision-making. The aim of this study is to predict weft defects in fabric production for a textile business using a multilayer perceptron model and multiple linear regression models. Matlab R2010b software was used for multilayer perceptron model solutions, and SPSS 13 packet software was used for multiple linear regression model solutions. The results of the two models were compared, and the multilayer perceptron model was identified as the best predictive model. This study shows that in operational research both artificial neural networks and the multiple linear regression model can be successfully used to predict fabric weft errors.
引用
收藏
页码：309 / 316
页数：8
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