Data filtering based recursive least squares estimation algorithm for a class of Wiener nonlinear systems

Based on the filtering theory, we present a filtering based recursive least squares algorithm for a class of Wiener nonlinear systems. The basic idea is to use an estimated noise transfer function to filter the input-ouput data, to obtain two identification models containing the parameters of the system model and the noise model, respectively, and to present the filtering based recursive least squares method to identify the parameters of these two models, by replacing the unmeasurable terms in the information vectors with their estimates. The illustrative example indicates that the proposed algorithm can generate more accurate parameter estimates compared with the recursive least squares algorithm.