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).
This research investigates how aggregation is currently conducted for simulation of large systems. The focus is on the
exploration of the different aggregation techniques for hierarchical lower-level (higher resolution) models into the next
higher-level. We develop aggregation procedures between two simulation levels (e.g., aggregation of mission level
models into a campaign level model) to address how much and what information needs to pass from the high-resolution
to the low-resolution model in order to preserve statistical fidelity. We present a mathematical representation of the
simulation model based on network theory and procedures for simulation aggregation that are logical and executable.
The proposed process is a collection of various conventional statistical and aggregation techniques, but we present them
in a coherent and systematic manner. Our desired real-world application for the developed simulation aggregation
process is in the area of military combat. We show preliminary results as applied to a complex hierarchical flying
training model. There is no best universal aggregation technique for different simulation models; however, the method
developed here is a well-defined set of procedures for statistically sound simulation model aggregation.