TY  - JOUR
VL  - 50
JF  - Automatica
IS  - 9
Y1  - 2014/09//
SP  - 2373 
PB  - Elsevier 
A1  - Piga, Dario
A1  - Tóth, Roland
EP  -  2380
ID  - eprints2476
UR  - http://www.sciencedirect.com/science/article/pii/S0005109814002969
KW  - Bias-corrected least-squares estimate; Nonlinear system identification; Output-error models
TI  - A bias-corrected estimator for nonlinear systems with output-error type model structures 
AV  - none
N2  - 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. 
SN  - 0005-1098
ER  -