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From: aam9n@helga2.acc.Virginia.EDU (Ali Ahmad Minai)
Newsgroups: comp.ai.neural-nets
Subject: Re: fault-tolerance of feedforward networks`
Keywords: fedforward networks, sensitivity, weight perturbation
Message-ID: <1991Apr29.045047.6176@murdoch.acc.Virginia.EDU>
Date: 29 Apr 91 04:50:47 GMT
References: <j+wg+7.@rpi.edu>
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In article <j+wg+7.@rpi.edu> omlinc@cs.rpi.edu (Christian Omlin) writes:
>Hi !
>
>I am running simulations with backprop networks. The network is used as
>a classifier.
>I am interested in the sensitivity of the network to perturbations
>in the weights. My experiments indicate that the performance degrades
>more rapidly when the weights from the input to the hidden layer are
>perturbed as opposed to perturbation of weights from the hidden to
>the output layer. This implies that, for my experiments, the shape
>of the decision regions is largely determined by the first hidden 
>layer. Are there any references (simulations, etc) confirming this
>behavior ?

The most directly relevant paper for this would be:

M. Stevenson, R. Winter & B. Widrow, "Sensitivity of Feedforward Neural
Networks to Weight Errors", IEEE Trans. on Neural Networks, vol. 1, no. 1,
pp. 71-80, March 1990.

I am currently working on the robustness of feed-forward nets with
real-valued outputs, but I am looking at perturbations in neuron
outputs. I have done what I think is a fairly thorough literature
search, but would appreciate any references, pointers etc. that
might address this issue. If there is interest, I will summarize
to the net. Thanks.

Any takers for a discussion of neural net fault-tolerance?

Regards,

Ali Minai
University of Virginia
aam9n@Virginia.EDU