  We address the important theoretical question why a recurrent neural network
with fixed weights can adaptively classify time-varied signals in the presence
of additive noise and parametric perturbations. We provide a mathematical proof
assuming that unknown parameters are allowed to enter the signal nonlinearly
and the noise amplitude is sufficiently small.
