Comparison Results Between Usual Backpropagation and Modified Backpropagation with Weighting: Application to Radar Detection

This paper presents some relevant results of a novel variant of the Backpropagation Algorithm to be applied during the Multilayer Perceptrons learning phase. The novelty consists in a weighting operation when the MLP learns the weights. The purpose is to modify the Mean Square Error objective giving more relevance to less frequent training patterns and resting relevance to the frequent ones. The inherent statistical distribution of training patterns is used to quantify how frequent a pattern is. The results, applied to a radar detector, show that Backpropagation with Weighting training requires much less training patterns maintaining the Artificial Neural Network performance.