BROWSE
Detail
Journal Article
재시작 기반 학습/추론 방법: 쿠프만 오토인코더의 미래 예측 성능 향상
- Issue Date
- 2024-08
- Citation
- 전기학회논문지, v.73, no.8, pp.1376-1383
- ISSN
- 1975-8359
- Publisher
- 대한전기학회
- Language
- Korean
- Type
- Journal Article
- Abstract
- Digital twin has gained attentions in the literature because it enables us to either estimate future states of a system or prevent its fault failures. To successfully apply digital twin into a system, a model of the system has to be accurately identified. Koopman Operator Theorey proved in 1931 sheds light on the system identification because it makes us interpret a nonlinear dynamical system as a linear system. Nowdays, deep learning technoloiges with the theory have been used for the system identification. We herein aim to review a model named as Koopman Autoencoder (KAE), which is considered to be baseline in the literature, and propose a new training/inference strategy to improve the performance of KAE for future estimation. To demonstrate advantages of our strategy, we develop two KAE models with/without our strategy, using synthetic datasets generated with the IEEE 3-Bus system. We show that KAE with the strategy can achieve a better performance with respect to mean squared error and relative root mean squared error, compared to KAE without it.
- KSP Keywords
- Digital Twin, Koopman Operator, Linear system, Nonlinear dynamical system, Synthetic Datasets, System identification(SI), bus system, deep learning(DL), mean square error(MSE), root mean square error(RMSE)
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC)
(CC BY NC)
