Improved frequency response function estimation by Gaussian process regression with prior knowledge

Kernel-based modelling of dynamical systems offers important advantages such as imposing stability, causality and smoothness on the estimate of the model. Here, we improve the existing frequency domain kernel-based approach for estimating the transfer function of a linear time-invariant system from noisy data. This is done by introducing prior knowledge in the kernel. We use a local rational modelling technique to determine the most significant poles, and include these poles as prior knowledge in the kernel. This results in accurate models for the identification of lightly-damped systems.

Publication year:2021

Keywords:Data-driven modelling, kernel-based, Gaussian process regression, machine learning, lightly damped systems, local rational modelling

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IOF-keylabel:yes

Accessibility:Open