BATTLEFIELD DECISION MAKING: A NEURAL NETWORK APPROACH
G. S. Gill, Jagdip Singh Sohal, Formerly Dean · 2008
Neural networks (NNs) have been increasingly used in recent years for the solving complex nonlinear problems. NNs are seen as an attractive alternative to process based modeling approaches, as they are able to extract an underlying relationship from the data when knowledge of physical process is lacking. The paper evaluates the predictive power of a model, which emulates an army commander on the battlefield when encountered with various situations, using two different neural network configurations – the multilayer perceptron (MLP) and the probabilistic neural network (PNN). The proposed model may prove effective to defence scientists and commanders for Battlefield decision making, strategy development and resource management. Empirical results support the potential of PNN as a better classifier, in training data as well as test data, compared to MLP.