  This paper introduces new techniques for using convex optimization to fit
input-output data to a class of stable nonlinear dynamical models. We present
an algorithm that guarantees consistent estimates of models in this class when
a small set of repeated experiments with suitably independent measurement noise
is available. Stability of the estimated models is guaranteed without any
assumptions on the input-output data. We first present a convex optimization
scheme for identifying stable state-space models from empirical moments. Next,
we provide a method for using repeated experiments to remove the effect of
noise on these moment and model estimates. The technique is demonstrated on a
simple simulated example.
