DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Choi, Han-Lim | - |
dc.contributor.advisor | 최한림 | - |
dc.contributor.author | Park, Young-Jin | - |
dc.date.accessioned | 2019-09-04T02:51:47Z | - |
dc.date.available | 2019-09-04T02:51:47Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843690&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/267312 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 항공우주공학과, 2019.2,[v, 71 p. :] | - |
dc.description.abstract | 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 | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Interpretable learning▼aunsupervised learning▼astate-space model▼agaussian processes | - |
dc.subject | 설명가능 학습▼a비지도 학습▼a상태공간 모델▼a가우시안 프로세스 | - |
dc.title | Interpretable unsupervised learning of bayesian nonparametric dynamic state-space model | - |
dc.title.alternative | 베이지안 비모수적 상태공간 모델의 설명가능 비지도 학습 기법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :항공우주공학과, | - |
dc.contributor.alternativeauthor | 박영진 | - |
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