Neural Networks and Their Application to Air Force Personnel Modeling

Neural network technology has recently demonstrated capabilities in areas important to personnel research such as statistical analysis, decision modeling, control, and forecasting. The present investigation indicates that three different neural network architectures are particularly suited to modeling many aspects of the Air Force personnel system: back propagation, learning vector quantization, and probabilistic neural networks. The primary advantage of neutral networks is their ability to derive nonlinear and interacting relationships among model variables. Two areas investigated in order to evaluate this capability were airmen reenlistment decisions and airman inventory modeling.