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Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training

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2 Author(s)
Murray, A.F. ; Dept. of Electr. Eng., Edinburgh Univ., UK ; Edwards, P.J.

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

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Neural Networks, IEEE Transactions on  (Volume:5 ,  Issue: 5 )