Synaptic Weight Noise During MLP Learning Enhances Fault-Tolerance, Generalization and Learning Trajectory

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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Alan Murray, Peter Edwards


We analyse the effects of analog noise on the synaptic arithmetic during MultiLayer Perceptron training, by expanding the cost func(cid:173) tion to include noise-mediated penalty terms. Predictions are made in the light of these calculations which suggest that fault tolerance, generalisation ability and learning trajectory should be improved by such noise-injection. Extensive simulation experiments on two distinct classification problems substantiate the claims. The re(cid:173) sults appear to be perfectly general for all training schemes where weights are adjusted incrementally, and have wide-ranging implica(cid:173) tions for all applications, particularly those involving "inaccurate" analog neural VLSI.