I__. - International Military Testing Association

In attempting to relate ASP score to demographic characteristics, an ordinary least squares regression was run.
The results yielded an ti =.213 and a root mean square error of 9.198. The large standard error is providing the
motivation to determine whether these results can be improved upon using a neural network approach that attempts
to map responses as opposed to total scores.
Neural Network Approach to Distortion Detection
The network paradigm that is currently being investigated is the cumulative backward error propagation
network. The network has fifty outputs representing individual responses. The hidden layer includes 120 ncurodes
and each uses a hyperbolic transfer function. The inputs include the same demographic information that was
hypothesized to be related to the ASP score in the earlier regressions. The network is shown graphically in Figure
2.
:umulativs Backward Error-Prnpagatton Network
Figure 2. Response Pattern Distortion Detection Network.
Due to the number of calculations that are involved in training the above network to recognize response pattern
distortion, a mainframe version of the cumulative backward error propagation neural network has been written for
the IBM 4381 and implemented at the Navy Personnel Research Development Center (NPRDC). The current
implementation is written in FORTRAN 77. Other network paradigms such as Grossberg’s Outstar , counter
propagation, and others are in the process of being added. Although, results arc extremely encouraging, it is
premature to report them at this time.
Summary
CertainIy neural network technology is still in its youth, nevertheless it has experienced significant growth in
recent years and the momentum shows no signs of slowing. Initially, the technology had a “black box” image, but
recent articles such as those by Ho&c and Funahashi demonstrate that neural networks is well founded in
mathematical theory and has statistical roots. That is to say, that a simple ordinary lcast squares regression can bc
expressed as a neural network, albeit a simple one. Neural networks have the potential for providing unique
approaches and insights into, heretofore, intractable problems. In the context of military manpower research, the
jury isn’t still deliberating, because all the evidence has not yet been presented. But when it is, we may find WC
have new answers to old problems.
29