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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 …
Study Of Human Factors Variables In Battle Outcome Prediction Models, David Andrew Glovier
Study Of Human Factors Variables In Battle Outcome Prediction Models, David Andrew Glovier
Engineering Management & Systems Engineering Theses & Dissertations
Over time there have been many improvements in models that are used to predict the outcome of battles. Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific evidence which shows that these factors do make a significant difference. This study investigates the use of factors that may impact on the human performance directly or indirectly in battle prediction models. These factors consist of traditional human factors and external factors that may influence the human performance. The research …