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Rapid, Low Cost Modeling and Simulation
Integrating Bayesian and Neural Networks as an Alternative to an Equation-Based Approach
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Modeling and Simulation is an important tool in manyfields of science and engineering. When properlyused, it can save time and money as compared toprototypes or experiments. Current practice is touse an equation-based approach. However, equation-based models that accurately represent the realworld can require extensive time and money toconstruct. This research was conducted to explorealternate methods of creating accurate models andsimulations that could be done rapidly and at muchlower cost. The research compared engineeringmodeling applications for time of construction andthe accuracy between equation-based models and threemethods of Bayesian network construction: humanjudgment, formulae and computer-generated withneural network integration. While the humanjudgment and formulae models showed no advantages,the computer-generated Bayesian networks were bothmuch faster to construct and more accurate ascompared to equivalent equation-based models.
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