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

Stable Nonlinear Identification From Noisy Repeated Experiments via Convex Optimization