  We propose a formulation for nonlinear recurrent models that includes simple
parametric models of recurrent neural networks as a special case. The proposed
formulation leads to a natural estimator in the form of a convex program. We
provide a sample complexity for this estimator in the case of stable dynamics,
where the nonlinear recursion has a certain contraction property, and under
certain regularity conditions on the input distribution. We evaluate the
performance of the estimator by simulation on synthetic data. These numerical
experiments also suggest the extent at which the imposed theoretical
assumptions may be relaxed.
