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NeuroCOLT
Technical Report NC-TR-00-066
Wolfgang
Maass
Eduardo D. Sontag
Abstract
We consider recurrent analog neural nets where the output of each
gate is subject to Gaussian noise, or any other common noise distribution
that is nonzero on a sufficiently large part of the state space. We
show that many regular languages cannot be recognized by networks
of this type, and we give a precise characterization of those languages
which can be recognized. This result implies severe constraints on
possibilities for constructing recurrent analog neural nets that are
robust against realistic types of analog noise. On the other hand
we present a method for constructing feedforward analog neural nets
that are robust with regard to analog noise of this type.
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