2004 NASA/DoD Conference on Evolvable Hardware (EH'04) Unsupervised Adaptation to Improve Fault Tolerance of Neural Network Classifiers Seattle, Washington, USA June 24-June 26 ISBN: 0-7695-2145-2
We investigate how to exploit the dynamics of unsupervised online learning rules for fault tolerance in neural network classifiers. We first design an adaptation mechanism that keeps neural network weights at a useful fixed point for classification problems. We then demonstrate the robustness of the system when the network inputs are subjected to faults.
Citation:
Alex Nugent, Garret Kenyon, Reid Porter, "Unsupervised Adaptation to Improve Fault Tolerance of Neural Network Classifiers," eh, pp.146, 2004 NASA/DoD Conference on Evolvable Hardware (EH'04), 2004 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||