Neural network models in simulation: a comparison with traditional modeling approaches

Neural models are enjoying a resurgence in systems research primarily due to a general interest in the connectionist approach to modeling in artificial intelligence and to the availability of faster and cheaper hardware on which neural net simulations can be executed. We have experimented with using a multi-layer neural network model as a simulation model for a basic ballistics model. In an effort to evaluate the efficiency of the neural net implementation for simulation modeling, we have compared its performance with traditional methods for geometric data fitting such as linear regression and surface response methods. Both of the latter approaches are standard features in many statistical software packages. We have found that the neural net model appears to be inadequate in most respects and we hypothesize that accuracy problems arise, primarily, because the neural network model does not capture the system structure characteristic of all physical models. We discuss the experimental procedure, issues and problems, and finally consider possible future research directions.