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Proceedings Paper

Building prediction models of large hierarchical simulation models with artificial neural networks and other statistical techniques
Author(s): June D. Rodriguez; Kenneth W. Bauer; John O. Miller; Robert E Neher
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Paper 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).

Paper Details

Date Published: 24 April 2008
PDF: 12 pages
Proc. SPIE 6978, Visual Information Processing XVII, 69780M (24 April 2008); doi: 10.1117/12.776715
Show Author Affiliations
June D. Rodriguez, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)
John O. Miller, Air Force Institute of Technology (United States)
Robert E Neher, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6978:
Visual Information Processing XVII
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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