Interpretable unsupervised learning of bayesian nonparametric dynamic state-space model = 베이지안 비모수적 상태공간 모델의 설명가능 비지도 학습 기법

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The goal of system identification is to learn about underlying physics dynamics behind the time-series data. To model the probabilistic and nonparametric dynamics model, Gaussian process (GP) have been widely used; GP can estimate the uncertainty of prediction and avoid over-fitting. Traditional GPSSMs, however, are based on Gaussian transition model, thus often have difficulty in describing a more complex transition model, e.g. aircraft motions. To resolve the challenge, this thesis proposes a framework using multiple GP transition models which is capable of describing multi-modal dynamics. Furthermore, this thesis extend the model to the information-theoretic framework, the so-called InfoSSM, by introducing a mutual information regularizer helping the model to learn interpretable and distinguishable multiple dynamics models. Two illustrative numerical experiments in simple Dubins vehicle and high-fidelity flight simulator are presented to demonstrate the performance and interpretability of the proposed model. Finally, this thesis introduces a framework using InfoSSM with Bayesian filtering for airplane tracking.
Advisors
Choi, Han-Limresearcher최한림researcher
Description
한국과학기술원 :항공우주공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 항공우주공학과, 2019.2,[v, 71 p. :]

Keywords

Interpretable learning▼aunsupervised learning▼astate-space model▼agaussian processes; 설명가능 학습▼a비지도 학습▼a상태공간 모델▼a가우시안 프로세스

URI
http://hdl.handle.net/10203/267312
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843690&flag=dissertation
Appears in Collection
AE-Theses_Master(석사논문)
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