<title>Applications of probabilistic neural networks</title>

Abstract

By replacing the sigmoid activation function often used in neural networks with an exponential function a probabilistic neural network (PNN) can be formed which computes nonlinear decision boundaries which are asymptotically Bayes-optimal. The PNN technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application the PNN paradigm was 200 times faster than back propagation. Many potential applications exist for neural networks of this type three recent investigations will be discussed in this paper. PNN has been used successfully to detect submarines based on hydrophone data. The neural network was trained to recognize characteristic spectra for both ships and submarines and was subsequently able to detect 1 00 of independent test sequences observed from the same class of submarines used for training with no false detections. PNN was applied to the problem of identifying types of ships based on analysis of electronic emissions from these ships (ELINT reports). The same technique was then applied to identification of land platforms based on ELINT reports. A combination of deterministic preprocessing and the PNN was used to deduce the underlying causes of satellite communications failures based on measurements of S/N for individual communications links. Data were supplied by the Defense Communications Agency. 1 . THE PROBABILISTIC NEURAL NETWORK There is a striking similarity between the organization