Initialization of output-error identification methods - Comparison between ARX and RPM models

Abstract

The main disadvantage of the Output-Error (OE) identification methods is that they may converge to a secondary optimum. A good initialization converges to the global optimum. The ARX model is often selected as initialization step to OE algorithms. However, the ARX model may be too biased and may not lead to a good initialization. This paper presents an approach based on the Reinitialized Partial Moment (RPM) to obtain a good initialization for OE methods. The RPM model presents an implicit filter that replaces the necessary explicit filter required by the ARX model. The result are encouraging and they have shown that the RPM based approach has to lead to a better initialization than the conventional techniques.