Data-Driven Impulse Response Regularization via Deep Learning
This work addresses system identification for engineers and researchers, but appears incremental as it builds on prior non-parametric models with deep learning.
The authors tackled impulse response estimation for linear systems by proposing a new data-driven model using deep learning, which they claim exploits more hidden patterns in input-output data compared to existing non-parametric models.
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.