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A Comparison of Artificial Neural Networks and Multiple Linear Regression Models As Predictors of Discard Rates In Plastic Injection Molding

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  • Vesile Sinem Arıkan Kargı

Abstract

In today’s global competitive environment, it is important to be able to evaluate the efficient use of a firms’ resources. The aim of this study is to predict the discard rate for headlight frames before the project of an automotive sub-industry firm in Bursa. For this prediction, the multilayer perceptron model, the radial basis function network model and multiple linear regression models were used. Matlab R2010b software was used for the multilayer perceptron model and radial basis function network solutions, and SPSS 13 packet software was used to solve the multiple linear regressions. Comparing the three models, the multilayer perceptron model was identified as the best predictive model.

Suggested Citation

  • Vesile Sinem Arıkan Kargı, 2015. "A Comparison of Artificial Neural Networks and Multiple Linear Regression Models As Predictors of Discard Rates In Plastic Injection Molding," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(2), pages 65-72, December.
    • Handle: RePEc:anm:alpnmr:v:3:y:2015:i:2:p:65-72
    DOI: http://dx.doi.org/10.17093/aj.2015.3.2.5000149667
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    References listed on IDEAS

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    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
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    More about this item

    Keywords

    Artificial Neural Networks; Discard Rate; Multilayer Perceptron Model; Multiple Linear Regression Model; Radial Basis Function Network Model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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