A graph subspace approach to system identification based on errors-in-variables system models
System identification based on the errors-in-variables (EIV) system model has been investigated by a number of people, led by Söderström and others. The total least-squares (TLS) algorithm is now well known, and has been effective for estimating the system parameters. In this paper, we first show that the TLS algorithm computes approximate maximum likelihood estimate (MLE) of the system parameters. Then we propose a graph subspace approach to tackle the same EIV identification problem, and derive a new estimation algorithm that is more general than the TLS algorithm. Two numerical examples are worked out to illustrate the proposed estimation algorithm for the EIV-based system identification.
Publication Source (Journal or Book title)
Kang, H., Gu, G., & Zheng, W. (2019). A graph subspace approach to system identification based on errors-in-variables system models. Automatica, 109 https://doi.org/10.1016/j.automatica.2019.108535