Host: The Japanese Society for Artificial Intelligence
Name : The 33rd Annual Conference of the Japanese Society for Artificial Intelligence, 2019
Number : 33
Location : [in Japanese]
Date : June 04, 2019 - June 07, 2019
In this talk, I introduce a data-driven method for modal decomposition of possibly time-varying dynamical systems. The formulation of the method is based on the probabilistic version of dynamic mode decomposition (DMD). While DMD and its many variants extract modes with fixed frequencies and decay rates, the proposed method aims to extract modes that have time-varying frequencies and decay rates. I show, with the results of preliminary experiments, that the proposed method works properly on time-varying dynamics.