@article{eprints2476,
         journal = {Automatica},
       publisher = {Elsevier },
          author = {Dario Piga and Roland T{\'o}th},
          volume = {50},
           month = {September},
          number = {9},
           pages = {2373 -- 2380},
           title = {A bias-corrected estimator for nonlinear systems with output-error type model structures },
            year = {2014},
        keywords = {Bias-corrected least-squares estimate; Nonlinear system identification; Output-error models},
        abstract = {Abstract Parametric identification of linear time-invariant (LTI) systems with output-error (OE) type of noise model structures has a well-established theoretical framework. Different algorithms, like instrumental-variables based approaches or prediction error methods (PEMs), have been proposed in the literature to compute a consistent parameter estimate for linear \{OE\} systems. Although the prediction error method provides a consistent parameter estimate also for nonlinear output-error (NOE) systems, it requires to compute the solution of a nonconvex optimization problem. Therefore, an accurate initialization of the numerical optimization algorithms is required, otherwise they may get stuck in a local minimum and, as a consequence, the computed estimate of the system might not be accurate. In this paper, we propose an approach to obtain, in a computationally efficient fashion, a consistent parameter estimate for output-error systems with polynomial nonlinearities. The performance of the method is demonstrated through a simulation example. },
             url = {http://eprints.imtlucca.it/2476/}
}