title: Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training.
abstract: We analyze the effects of analog noise on the synaptic arithmetic during multilayer perceptron training, by expanding the cost function to include noise-mediated terms. Predictions are made in the light of these calculations that suggest that fault tolerance, training quality and training trajectory should be improved by such noise-injection. Extensive simulation experiments on two distinct classification problems substantiate the claims. The results appear to be perfectly general for all training schemes where weights are adjusted incrementally, and have wide-ranging implications for all applications, particularly those involving "inaccurate" analog neural VLSI.
authors.affiliation: Dept. of Electr. Eng., Edinburgh Univ.
authors.forename: A F
authors.initials: AF
authors.lastname: Murray
authors.forename: P J
authors.initials: PJ
authors.lastname: Edwards