Robust recursive least-squares method with modified weights for bilinear system identification

The least-squares method is one of the most efficient and simple identification methods commonly used. Unfortunately, it is very sensitive to large errors (outliers)in the input/output data. In such cases, it may never converge or give erroneous results. In practice, most real systems are nonlinear. Many of these can be suitably represented by bilinear models. In the paper, a robust recursive least-squares method has been proposed for bilinear system identification. It differs from earlier approaches in that it uses modified weights in the criterion for robustness. A theorem proving the convergence of the proposed algorithms included. Results of the simulation demonstrating the robustness of the proposed algorithm are also included.