FAULT TOLERANT MODELING FOR ARTIFICIAL NEURAL NETWORKS ARCHITECTURE

Abstract

Artificial Neural Networks are composed of a large number of simple computational units operating in parallel they have the potential to provide fault tolerance. One extremely motivating possessions of genetic neural networks of the additional urbanized human body and other animal is their open-mindedness against injure or destroyed to individual neurons. In the case of biological neural networks a solution tolerant to loss of neurons has a high priority since a graceful degradation of performance is very important to the survival of the organism. We propose a simple modification of the training procedure commonly used with the Back-Propagation algorithm in order to increase the tolerance of the feed forward multi-layered ANN to internal hardware failures such as the loss of hidden units.

Key Word: ANN, Fault, Tolerant, Real, MLP 0976