The adaptive weight using RAM

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.