Data-Driven Impulse Response Regularization via Deep Learning. Issue 15 (2018)

• Record Type: Journal Article
• Authors: Andersson, Carl; Wahlström, Niklas; Schön, Thomas B.
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 15(2018)
• Page Start: 1
• Page End: 6
• Publication Date: 2018
• Subjects: Linear system identification -- impulse response estimation -- flexible models -- deep learning -- regularization -- Gaussian processes; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.081
• Languages: English
• ISSNs: 2405-8963

Abstract

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.