Nonparametric Identification of Linear Time-Varying Systems using Gaussian Process Regression. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Hallemans, N.; Lataire, J.; Pintelon, R.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 1001
• Page End: 1006
• Publication Date: 2020
• Subjects: Identification -- Linear time-varying systems -- Gaussian processes -- Machine learning; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1277
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Linear time-varying systems are a class of systems, the dynamics of which evolve in time. This results in a time-varying frequency response function where each frequency has a time-varying gain. In classical identification techniques, basis functions are employed to fit these time-varying gains. In this paper a new method based on Gaussian process regression is presented. The advantage of the proposed method is a more convenient model structure and model order selection.