Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data
Signal Processing
2019-11-18 v2 Machine Learning
Dynamical Systems
Abstract
Koopman Mode Decomposition (KMD) is a technique of nonlinear time-series analysis that originates from point spectrum of the Koopman operator defined for an underlying nonlinear dynamical system. We present a numerical algorithm of KMD based on Gaussian process regression that is capable of handling noisy finite-time data. The algorithm is applied to short-term swing dynamics of a multi-machine power grid in order to estimate oscillatory modes embedded in the dynamics, and thereby the effectiveness of the algorithm is evaluated.
Keywords
Cite
@article{arxiv.1911.01143,
title = {Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data},
author = {Akitoshi Masuda and Yoshihiko Susuki and Manel Martínez-Ramón and Andrea Mammoli and Atsushi Ishigame},
journal= {arXiv preprint arXiv:1911.01143},
year = {2019}
}
Comments
7 pages, 6 figures, 2019 Japan Joint Automatic Control Conference