Improving the tolerance of multilayer perceptrons by minimizing the statistical sensitivity to weight deviations

This paper proposes a version of the backpropagation algorithm which increases the tolerance of a feedforward neural network against deviations in the weight values. These changes can originate either when the neural network is mapped on a given VLSI circuit where the precision and/or weight matching are low, or by physical defects affecting the neural circuits. The modified backpropagation algorithm we propose uses the statistical sensitivity of the network to changes in the weights as a quantitative measure of network tolerance and attempts to reduce this statistical sensitivity while keeping the figures for the usual training performance (in errors and time) similar to those obtained with the usual backpropagation algorithm. (C) 2000 Elsevier Science B.V. All rights reserved.