Korean Management Science Review (경영과학)

The Korean Operations Research and Management Science Society (한국경영과학회)
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A comparison of neural networks to ols regression in process/quality control applications

  • Nam, Kyungdoo (Business Administration Division, University) ;
  • Sanford, Clive C. (Business Computer Information Systems Department College of Business Administration University of North Texas) ;
  • Jayakumar, Maliyakal D. (Business Computer Information Systems Department College of Business Administration University of North Texas)
  • Published : 1994.06.01
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Abstract

This study compares the performance of neural networks and ordinary least squares regression with quality-control processes. We examine the applicability of neural networks because they do not require any assumptions regarding either the functional from of the underlying process or the distribution of errors. The coefficient of determination($R^2$), mean absolute deviation(MAD), and the mean squared error(MSE) metrics indicate that neural networks are a viable and can be a superior technique. We also demonstrate that an assessment of the magnitude of the neural notwork input layer cumulative weights can be used to determine the relative importance of predictor variables.

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