An Accurate Measure for Multilayer Perceptron Tolerance to Weight Deviations

The inherent fault tolerance of artificial neural networks (ANNs) is usually assumed, but several authors have claimed that ANNs are not always fault tolerant and have demonstrated the need to evaluate their robustness by quantitative measures. For this purpose, various alternatives have been proposed. In this paper we show the direct relation between the mean square error (MSE) and the statistical sensitivity to weight deviations, defining a measure of tolerance based on statistical sentitivity that we have called Mean Square Sensitivity (MSS); this allows us to predict accurately the degradation of the MSE when the weight values change and so constitutes a useful parameter for choosing between different configurations of MLPs. The experimental results obtained for different MLPs are shown and demonstrate the validity of our model.