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The Effect Of Model Formulation On The Comparative Performance Of Artificial Neural Networks And Regression, Michael F. Cochrane
The Effect Of Model Formulation On The Comparative Performance Of Artificial Neural Networks And Regression, Michael F. Cochrane
Engineering Management & Systems Engineering Theses & Dissertations
Multiple linear regression techniques have been traditionally used to construct predictive statistical models, relating one or more independent variables (inputs) to a dependent variable (output). Artificial neural networks can also be constructed and trained to learn these complex relationships, and have been shown to perform at least as well as linear regression on the same data sets. Research on the use of neural network models as alternatives to multivariate linear regression has focused predominantly on the effects of sample size, noise, and input vector size on the comparative performance of these two modeling techniques. However, research has also shown that …