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Nonparametric Identification of Linear Time-Varying Systems using Gaussian Process Regression⁎This research was supported in part by the Fund for Scientific Research (FWO Vlaanderen), and in part by the Flemish Government (Methusalem Grant METH1). Issue 2 (2020)
Record Type:
Journal Article
Title:
Nonparametric Identification of Linear Time-Varying Systems using Gaussian Process Regression⁎This research was supported in part by the Fund for Scientific Research (FWO Vlaanderen), and in part by the Flemish Government (Methusalem Grant METH1). Issue 2 (2020)
Main Title:
Nonparametric Identification of Linear Time-Varying Systems using Gaussian Process Regression⁎This research was supported in part by the Fund for Scientific Research (FWO Vlaanderen), and in part by the Flemish Government (Methusalem Grant METH1)
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.