Abstract
Since most real processes are nonlinear systems, nonlinear models are required for accurate characterization of the systems. However, nonlinear identification techniques have been less developed. In this paper, we proposed an iterative method to identify nonlinear systems that can be described by a nonlinear Wiener model. The result shows that the model parameters converge quickly to the asymptote, and prediction error is greatly reduced by the model parameter optimization. For the future control of Wiener systems, an Extended Kalman Filter-based state estimator is designed, which allows the linear control technique to be applied to nonlinear Wiener systems directly. The numerical simulations show that the proposed algorithm is effective and has a low computational cost for identification, and the states of the Wiener model are estimated successfully.
