Paper Info
Title
Factorially Switching Dynamic Mode Decomposition For Koopman Analysis Of Time-Variant Systems
Abstract
The modal decomposition based on the spectra of the Koopman operator has gained much attention in various areas such as data science and optimal control, and dynamic mode decomposition (DMD) has been known as a data-driven method for this purpose. However, there is a fundamental limitation in DMD and most of its variants; these methods are based on the premise that the target system is time-invariant at least within the data at hand. In this work, we aim to compute DMD on time-varying dynamical systems. To this end, we propose a probabilistic model that has factorially switching dynamic modes. In the proposed model, which is based on probabilistic DMD, observation at each time is expressed using a subset of dynamic modes, and the activation of the dynamic modes varies over time. We present an approximate inference method using expectation propagation and demonstrate the modeling capability of the proposed method with numerical examples of temporally-local events and transient phenomena.
Year
DOI
Venue
2018
10.1109/CDC.2018.8619846
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Dynamic mode decomposition,Optimal control,Computer science,Control theory,Algorithm,Approximate inference,Dynamical systems theory,Operator (computer programming),Statistical model,Expectation propagation,Probabilistic logic
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Naoya Takeishi1307.16
Takehisa Yairi229429.82
Kawahara, Yoshinobu331731.30