DocumentCode :
1841857
Title :
Components for a sequence processing neural network
Author :
Calvert, D. ; Stacey, D.A.
Author_Institution :
Dept. of Comput. & Inf. Sci., Guelph Univ., Ont., Canada
Volume :
3
fYear :
1999
fDate :
1999
Firstpage :
1568
Abstract :
Describes the extension of an artificial neural network for sequence modeling and recall. This basic system learns an initial data set which it uses as an exemplar for later sequence processing. This system is designed to take full advantage of the features common to artificial neural networks. The extension to this system removes a limitation found in the initial architecture which prevented it from predicting sequence values from outside the range of the training dataset
Keywords :
ART neural nets; learning (artificial intelligence); modelling; neural net architecture; self-organising feature maps; sequences; recall; sequence modeling; sequence processing neural network; Artificial neural networks; Helium; Impedance matching; Information science; Neural networks; Resonance; Shape; Subspace constraints; Testing; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.832604
Filename :
832604
Link To Document :
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