Learning-based on-line testing in feedforward neural networks
Naotake Kamiura, K. Yamato, Teijiro Isokawa, Nobuyuki Matsui · 2003
Learning-based on-line testing in feedforward neural networks (NNs) is discussed. After the convergence of the ordinary learning, the re-learning employing two sigmoid activation functions per neuron in the last layer of the NN is made. It sets up the range of erroneous potentials produced from the last layer, and enables us to detect faults without extra hardware.