Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting

arXiv:2404.15098v1 Announce Type: cross
Abstract: We revisit the problem of predicting the output of an LTI system directly using offline input-output data (and without the use of a parametric model) in the behavioral setting. Existing works calculate the output predictions by projecting the recent samples of the input and output signals onto the column span of a Hankel matrix consisting of the offline input-output data. However, if the offline data is corrupted by noise, the output prediction is no longer …

abstract arxiv cs.lg cs.sy data data-driven eess.sy error input-output offline parametric predictions quantification samples type uncertainty