24 April 2008 Building prediction models of large hierarchical simulation models with artificial neural networks and other statistical techniques
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Abstract
The purpose of this research is to examine how to achieve suitable aggregation in the simulation of large systems. More specifically, investigating how to accurately aggregate hierarchical lower-level (higher resolution) models into the next higher-level in order to reduce the complexity of the overall simulation model. The initial approach used in this research was to use a realistic simulation model of a complex flying training model to apply the model aggregation methodologies using artificial neural networks and other statistical techniques. In order to test the techniques proposed, we modified a flying training model built for another study to suit the needs of our experiment. The study examines the effectiveness of three types of artificial neural networks as a metamodel in predicting outputs of the flying training model. Feed-forward, radial basis function, and generalized regression neural networks are considered and are compared to the truth simulation model, where the truth model is when actual lower-level model outputs are used as a direct input into the next higher-level model. The desired real-world application of the developed simulation aggregation process will be applied to military combat modeling in the area of combat identification (CID).
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June D. Rodriguez, Kenneth W. Bauer, John O. Miller, Robert E Neher, "Building prediction models of large hierarchical simulation models with artificial neural networks and other statistical techniques", Proc. SPIE 6978, Visual Information Processing XVII, 69780M (24 April 2008); doi: 10.1117/12.776715; https://doi.org/10.1117/12.776715
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