KUMAŞ ATKI HATASI TAHMİNİNDE YAPAY SİNİR AĞLARI VE ÇOKLU DOĞRUSAL REGRESYON MODELLERİNİN KARŞILAŞTIRILMASI

Firmalar için belirsizliğin tahmini yöneticiler tarafından alınan kararların güvenilirliği için oldukça önemlidir. Günümüz problemleri karmaşık ve çözümü de bilimsel karar vermeyi gerektirir. Bu çalışmanın amacı bir tekstil firmasının kumaş üretiminde ortaya çıkan atkı hatalarını önceden tahmin etmektir. Bu tahmin için çok katmanlı algılayıcı model ve çoklu doğrusal regresyon model teknikleri kullanılmıştır. Çalışmada çok katmanlı algılayıcı model çözümleri için Matlab R2010b programı, çoklu doğrusal regresyon model çözümü için SPSS 13 paket programı kullanılmıştır. Firmanın kumaş atkı hata tahmininde bu iki model kıyaslanmış ve en uygun modelin çok katmanlı algılayıcı model olduğu belirlenmiştir. Bu çalışma yöneylem araştırması tekniklerinden yapay sinir ağ ve çok değişkenli regresyon modellerinin kumaş atkı hatalarının tahmininde faydalı bir araç olarak kullanılabileceğini göstermektedir

Anahtar Kelimeler:

Yapay Sinir Ağları, Çok Katmanlı Algılayıcı Model, Çoklu Doğrusal Regresyon Model, Kumaş Atkı Hatası, Tahmin

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

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

Keywords:

Artificial neural network, Multilayer perceptron model, Multiple linear regression model, Fabric weft defect, Prediction,

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