By Topic

An adaptive data sorter based on probabilistic neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wang, C.D. ; Ail Syst. Inc., Melville, NY, USA ; Thompson, J.P.

Based on a self-organized, probabilistic neural network (PNN) paradigm, a parallel network can be used to sort data parameters into classes with high-sorting accuracy and low fragmentation. The capabilities of the sorter, as applied to ESM (electronic support measure) pulse-data sorting, are shown. The PNN implements the statistical Bayesian strategy by computing a joint probability density over all input data parameters to match a group of candidate data classes. The sorting is accomplished by assigning the inputs to the most likely group with highest probability density estimate. Based on test data from an ESM system, the PNN has shown significant improvement over conventional rule-based techniques. The parallel computer architecture of PNN is well-suited for VLSI chip implementation. An 80000 gate semicustom chip design is described

Published in:

Aerospace and Electronics Conference, 1991. NAECON 1991., Proceedings of the IEEE 1991 National

Date of Conference:

20-24 May 1991