Summary
International Symposium on Nonlinear Theory and its Applications
2017
Session Number:A1L-A
Session:
Number:A1L-A-4
Modeling Nonlinear Dynamic System in RKHS through the Koopman Operator
Satomi Sugaya, Yoshihiko Susuki, Atushi Ishigame, Andrea Mammoli, Manel Martinez-Ramon,
pp.7-10
Publication Date:2017/12/4
Online ISSN:2188-5079
Summary:
Koopman Operator is a linear but infinite-dimensional operator defined for a nonlinear dynamical system and captures full information of the system. We present a formulation in Reproduced Kernel Hirbert Space (RKHS) for modeling a nonlinear dynamic system in order to develop relevant linear estimators. The KO is represented as a linear estimator in RKHS, and its parameters are determined using the well-known Gaussian process models. This leads to structures useable in modeling and nowcasting that account for the nonlinear behavior of the system.