DocumentCode
1089888
Title
Comments on "Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution"
Author
Kremer, S.C.
Author_Institution
Communication Res. Centre, Ottawa, Ont., Canada
Volume
7
Issue
4
fYear
1996
fDate
7/1/1996 12:00:00 AM
Firstpage
1047
Lastpage
1051
Abstract
Giles et al. (1995) have proven that Fahlman´s recurrent cascade correlation (RCC) architecture is not capable of realizing finite state automata that have state-cycles of length more than two under a constant input signal. This paper extends the conclusions of Giles et al. by showing that there exists a corollary to their original proof which identifies a large second class of automata, that is also unrepresentable by RCC.
Keywords
automata theory; learning (artificial intelligence); recurrent neural nets; constructive learning; finite state automata; recurrent cascade correlation; recurrent neural networks; state-cycles; Labeling; Learning automata; Neural networks; Recurrent neural networks; Sun;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.508949
Filename
508949
Link To Document