On the efficiency of OLS reduced probabilistic neural networks for aircraft-flare discrimination

Probabilistic neural networks (PNN) are the instruments of choice when it comes to critical decision making. Indeed, their output is not a simple yes-or-no decision; they are able to produce the probability that the features received as their input correspond to an object of any one of many classes. In work reported elsewhere, we have devised such a network for the discrimination of aircrafts from their decoy flares. It is very efficient, consistently exhibiting a recognition success rate of the order of 98-99%. However, because these neural networks are based on the Parzen-windows method, they must contain a very large number of neurons in order to be efficient. This can represent a serious disadvantage when they are to be incorporated in a real time system. It is thus advantageous to be able to reduce their size, without affecting appreciably their performance. We report in this article on the success we have had with adapting and applying an Orthogonal Least Squares (OLS) reduction method to the probabilistic neural network we built previously. We show that this method allows for a reduction of the number of neurons by as much as 81.9% with a decrease in performance of only 0.6%. Even a drastic reduction of 97.7% of number of neurons still produces a network with a 93.5% success rate. A side benefit of the application of this method to PNNs, is an ordered list of the images that the neural network considers as the best representatives of their class of objects.