Modeling of component failure in neural networks for robustness evaluation: an application to object extraction

Abstract

The robustness of neural network (NN) based information processing systems with respect to component failure (damaging of nodes/links) is studied. The damaging/component failure process has been modeled as a Poisson process. To choose the instants or moments of damaging, statistical sampling technique is used. The nodes/links to be damaged are determined randomly. As an illustration, the model is implemented and tested on different object extraction algorithms employing Hopfield's associative memory model, Gibbs random fields, and a self-organizing multilayer neural network. The performance of these algorithms is evaluated in terms of percentage of pixels correctly classified under different noisy environments and different degrees and sequences of damaging. The deterioration in the output is seen to be very small even when a large number of nodes/links are damaged.