dc.citation.conferencePlace |
CN |
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dc.citation.conferencePlace |
Quebec City |
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dc.citation.endPage |
28 |
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dc.citation.startPage |
22 |
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dc.citation.title |
28th AAAI Conference on Artificial Intelligence, AAAI 2014 |
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dc.contributor.author |
Choi, Jaesik |
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dc.contributor.author |
Amir, E |
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dc.contributor.author |
Xu, T |
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dc.contributor.author |
Valocchi, AJ |
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dc.date.accessioned |
2023-12-19T23:38:57Z |
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dc.date.available |
2023-12-19T23:38:57Z |
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dc.date.created |
2019-03-25 |
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dc.date.issued |
2014-07-27 |
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dc.description.abstract |
The Kalman Filter (KF) is pervasively used to control a vast array of consumer, health and defense products. By grouping sets of symmetric state variables, the Relational Kalman Filter (RKF) enables to scale the exact KF for large-scale dynamic systems. In this paper, we provide a parameter learning algorithm for RKF, and a regrouping algorithm that prevents the degeneration of the relational structure for efficient filtering. The proposed algorithms significantly expand the applicability of the RKFs by solving the following questions: (1) how to learn parameters for RKF in partial observations; and (2) how to regroup the degenerated state variables by noisy real-world observations. We show that our new algorithms improve the efficiency of filtering the large-scale dynamic system. |
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dc.identifier.bibliographicCitation |
28th AAAI Conference on Artificial Intelligence, AAAI 2014, pp.22 - 28 |
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dc.identifier.issn |
0000-0000 |
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dc.identifier.scopusid |
2-s2.0-84974827226 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/35001 |
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dc.identifier.url |
https://dl.acm.org/citation.cfm?id=2908343 |
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dc.language |
영어 |
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dc.publisher |
AI Access Foundation |
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dc.title |
Parameter estimation for Relational Kalman Filtering |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2014-07-27 |
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