NDLI: An application of neural net technology to surveillance information correlation and battle outcome prediction

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Proceedings of the IEEE 1988 National Aerospace and Electronics Conference

An application of neural net technology to surveillance information correlation and battle outcome prediction

Content Provider	IEEE Xplore Digital Library
Author	Maloney, P.S.
Copyright Year	1989
Description	Author affiliation: Lockheed Missiles & Space Co. Inc., Austin, TX, USA (Maloney, P.S.)
Abstract	The author describes a three-layer probabilistic feed-forward neural network that uses sums of Gaussian distributions to estimate the probability density function for a training data set. She shows how this trained network can be used to classify new data sets and to provide a probability associated with each classification. The method has been applied successfully to two separate electronic intelligence emitter correlation problems (hull-to-emitter and land-based emitter correlation). Each of these applications achieved a high degree of accuracy in identifying the correct emitter among many possible emitters about 200000 times faster than the standard back-propagation neural network technique. To show the versatility of the probabilistic neural network for performing optimally any classification problem, an application to battle outcome prediction is described.<>
Starting Page	948
Ending Page	955
File Size	675318
Page Count	8
File Format	PDF
DOI	10.1109/NAECON.1989.40326
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1989-05-22
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Surveillance Probability density function Space technology Intelligent networks Computer aided manufacturing Computer networks Missiles Feedforward systems Feedforward neural networks
Content Type	Text
Resource Type	Article

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