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6 x 9, illustrated
ISSN
0899-7667
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1530-888X
2014 Impact factor:
2.21

Neural Computation

Winter 1990, Vol. 2, No. 4, Pages 502-509
(doi: 10.1162/neco.1990.2.4.502)
© 1990 Massachusetts Institute of Technology
Convergence Properties of Learning in ART1
Article PDF (464.57 KB)
Michael Georgiopoulos
Department of Electrical Engineering, University of Central Florida, Orlando, FL 32816 USA
Gregory L. Heileman
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87132 USA
Juxin Huang
Department of Electrical Engineering, University of Central Florida, Orlando, FL 32816 USA
Abstract

We consider the ART1 neural network architecture. It is shown that in the fast learning case, an ART1 network that is repeatedly presented with an arbitrary list of binary input patterns, self-stabilizes the recognition code of every size-l pattern in at most l list presentations.