Paper:
The Adaptive Weight-using RAM Model
SIMÕES, E.V., UEBEL, L.F., UENO, Y., BARONE, D.A.C.
1997 IEEE International Conference on Systems, Man and Cybernetics, Hyatt Orlando, Orlando, Florida, USA, Oct., 1997.
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Abstract:
This article analyses the saturation problem of a RAM neural network, a n-tuple classifier
containing 340 12-input neurons applied to the character recognition task, using the
British mail data bank. It presents data to evaluate this problem and correlates it to
other characteristics of the RAM nets. Therefore, two novel approaches were suggested
to reduce the network saturation and improve the recognition level: the Filtered RAM and the
Adaptive Weight Using RAM (AWURAM). The first version simply multiplies each input vector by
a digital filter during the training and the recall phases. The second approach associates
the weight concept to the network in order to distinguish different regions among the trained
classes.
Eduardo do Valle Simões,
simoes@icmc.usp.br
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