dc.citation.conferencePlace |
KO |
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dc.citation.title |
대한산업공학회/한국경영과학회 2006 춘계공동학술대회 |
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dc.contributor.author |
Shin, Moon Soo |
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dc.contributor.author |
Jung, Mooyoung |
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dc.date.accessioned |
2023-12-20T05:09:12Z |
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dc.date.available |
2023-12-20T05:09:12Z |
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dc.date.created |
2014-12-23 |
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dc.date.issued |
2006-05-01 |
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dc.description.abstract |
Fractal manufacturing system (FrMS) distinguishes itself from other manufacturing systems by the fact that there is a fractal repeated at every scale. A fractal is a volatile organization which consists of goal-oriented agents referred to as AIR-units (autonomous and intelligent resource units). AIR-units unrestrictedly reconfigure fractals in accordance with their own goals. Their goals can be dynamically changed along with the environmental status. Since goals of AIR-units are represented as fuzzy models, an AIR-unit itself is a fuzzy logic controller. This paper presents a goal regulation mechanism in the FrMS. In particular, a reinforcement learning method is adopted as a regulating mechanism of the fuzzy goal model, which uses only weak reinforcement signal. Goal regulation is achieved by building a feedforward neural network to estimate compatibility level of current goals, which can then adaptively improve compatibility by using the gradient descent method. Goal-oriented features of AIR-units are also presented. |
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dc.identifier.bibliographicCitation |
대한산업공학회/한국경영과학회 2006 춘계공동학술대회 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/51958 |
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dc.publisher |
대한산업공학회/한국경영과학회 |
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dc.title.alternative |
Goal regulation mechanism through reinforcement learning in a fractal manufacturing system (FrMS) |
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dc.title |
프랙탈 생산시스템에서의 강화학습을 통한 골 보정 방법 |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2006-05-01 |
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