  A recurrent neural net is described that learns a set of patterns in the
presence of noise. The learning rule is of Hebbian type, and, if noise would be
absent during the learning process, the resulting final values of the weights
would correspond to the pseudo-inverse solution of the fixed point equation in
question. For a non-vanishing noise parameter, an explicit expression for the
expectation value of the weights is obtained. This result turns out to be
unequal to the pseudo-inverse solution. Furthermore, the stability properties
of the system are discussed.
