By Topic

Comparison of Adaptive Resonance Theory Neural Networks for Astronomical Region of Interest Detection and Noise Characterization

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
$33 $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

6 Author(s)
Robert J. Young ; Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. email: ; Mike Ritthaler ; Peter Zimmer ; John McGraw
more authors

While learning algorithms have been used for astronomical data analysis, the vast majority of those algorithms have used supervised learning. In a continuation of the work described in Young et ah [18] we examine the use of unsupervised learning for this task with two types of Adaptive Resonance Theory (ART) neural networks. Using synthetic astronomical data from SkyMaker[2], [3] which was designed to mimic the dynamic range of the CTI-[14] telescope, we compared the ability of the ART-1 neural network[4] and the ART-1 neural network with category theoretic modiflcation[9], [11] to detect regions of interest and to characterize noise. We show a difference in the geometries of the templates created by each architecture. We also show an analysis of the two architectures over a range of parameter settings. The results provided show that ART neural networks and unsupervised learning algorithms in general should not be overlooked for astronomical data analysis.

Published in:

2007 International Joint Conference on Neural Networks

Date of Conference:

12-17 Aug. 2007