Model evaluation of stochastic subspace identification methods by means of spectral analisys

Abstract

Stochastic subspace identification methods have several integer parameters such as the model order and the number of block rows of data matrices. In order to give a guideline for choosing such parameters, this paper proposes a criterion for evaluating the models estimated by means of subspace identification algorithms. The proposed method is based on the power spectral density which is approximatelly computed from the given time-series data.