Influence on ANNs fault tolerance of binary errors introduced during training

This paper presents the effect of binary errors on artificial neural networks during the training phase and the fault tolerance of ANN. The tested network implements a problem (recognition of digits and letters) and is trained in the presence of binary errors. A significant improvement is obtained with increase in the number of perturbations during the training phase and recognition rate during the generalization phase.