Linear and non-linear system identification using separable least-squares

摘要

We demonstrate how the separable least-squares technique of Golub and Pereyra can be exploited in the identification of both linear and non-linear systems based on the prediction error formulation. The model classes to be considered here are the output error model and innovation model in the linear case and the Wiener system in the non-linear case. This technique together with a suitable choice of parameterization allow us to solve, in the linear case, the associated optimization problem using only np parameters instead of the usual n(m+p)+mp parametrs when canonical and outputs. An example where the Wiener system identification algorithm is applied to real data obtained from a distillation column is given.