On Making Neural Network Based Learning Systems Robust
Ashish Kumar Ghosh, Hideo Tanaka · IETE Journal of Research · 1998
A method for making nerual network based learning systems robust with respect to component failure (damaging of nodes/links) is suggested in the present investigation. The method allows some of the components to fail at various instants of the entire learning process. The change in error value caused by this damage will be adjusted while the other components learn their parameters during the rest part of learning. The damaging/component failure process has been modeled as a Poisson process. The instants or moments of damaging are chosen by statistical sampling. The components to be damaged are determined randomly. As an illustration, the model is implemented on the back-propagation learning algorithm.