%V 50
%I Elsevier 
%T A bias-corrected estimator for nonlinear systems with output-error type model structures 
%P 2373 - 2380
%N 9
%J Automatica
%R 10.1016/j.automatica.2014.07.021
%K Bias-corrected least-squares estimate; Nonlinear system identification; Output-error models
%A Dario Piga
%A Roland T?th
%X 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. 
%D 2014
%L eprints2476