Online Identification of Stochastic Continuous-Time Wiener Models Using
  Sampled Data
Mohamed Abdalmoaty Efe C. Balta John Lygeros Roy S. Smith
It is well known that ignoring the presence of stochastic disturbances in the
identification of stochastic Wiener models leads to asymptotically biased
estimators. On the other hand, optimal statistical identification, via
likelihood-based methods, is sensitive to the assumptions on the data
distribution and is usually based on relatively complex sequential Monte Carlo
algorithms. We develop a simple recursive online estimation algorithm based on
an output-error predictor, for the identification of continuous-time stochastic
parametric Wiener models through stochastic approximation. The method is
applicable to generic model parameterizations and, as demonstrated in the
numerical simulation examples, it is robust with respect to the assumptions on
the spectrum of the disturbance process.