Data-Driven Impulse Response Regularization via Deep Learning
Systems and Control
2018-10-12 v2 Machine Learning
Machine Learning
Abstract
We consider the problem of impulse response estimation of stable linear single-input single-output systems. It is a well-studied problem where flexible non-parametric models recently offered a leap in performance compared to the classical finite-dimensional model structures. Inspired by this development and the success of deep learning we propose a new flexible data-driven model. Our experiments indicate that the new model is capable of exploiting even more of the hidden patterns that are present in the input-output data as compared to the non-parametric models.
Cite
@article{arxiv.1801.08383,
title = {Data-Driven Impulse Response Regularization via Deep Learning},
author = {Carl Andersson and Niklas Wahlström and Thomas B. Schön},
journal= {arXiv preprint arXiv:1801.08383},
year = {2018}
}