An improvement in weight-fault tolerance of feedforward neural networks
This paper proposes feedforward neural networks (NNs) tolerating stuck-at faults of weights. To cope with faults having small false absolute values, the potential calculation of the neuron is modified, and the gradient of activation function is steepened. To cope with faults having large absolute va...
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