Identification of the smallest unfalsified model set based on stochastic noisy data

Abstract

We propose a new model set identification method using experimental data contaminated by stochastic noise. We find the smallest model set which is consistent with the experimental data by separating the output error into the deterministic part due to the unmodeled dynamics and the stochastic noise part. Furthermore, the effectiveness of this method is shown by numerical examples.