Design of fuzzy learning control systems for steam generator water level control = 증기발생기 수위제어를 위한 퍼지학습 제어시스템 개발에 관한 연구

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dc.contributor.advisorSeong, Poong-Hyun-
dc.contributor.advisor성풍현-
dc.contributor.authorPark, Gee-Yong-
dc.contributor.author박기용-
dc.date.accessioned2011-12-14T08:03:52Z-
dc.date.available2011-12-14T08:03:52Z-
dc.date.issued1996-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=108790&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/48862-
dc.description학위논문(박사) - 한국과학기술원 : 원자력공학과, 1996.8, [ xiv, 179 p. ]-
dc.description.abstractA fuzzy learning algorithm is developed in order to construct the useful control rules and tune the membership functions in the fuzzy logic controller used for water level control of nuclear steam generator. The fuzzy logic controllers have shown to perform better than conventional controllers for ill-defined or complex processes such as nuclear steam generator. Whereas the fuzzy logic controller does not need a detailed mathematical model of a plant to be controlled, its structure is to be made on the basis of the operator``s linguistic information experienced from the plant operations. It is not an easy work and also there is no systematic way to translate the operator``s linguistic information into quantitative information. When the linguistic information of operators is incomplete, tuning the parameters of fuzzy controller is to be performed for better control performance. It is the time and effort consuming procedure that controller designer has to tune the structure of fuzzy logic controller for optimal performance. And if the number of control inputs is many and the rule base is constructed in multidimensional space, it is very difficult for a controller designer to tune the fuzzy controller structure. Hence, the difficulty in putting the experimental knowledge into quantitative (or numerical) data and the difficulty in tuning the rules are the major problems in designing fuzzy logic controller. In order to overcome the problems described above, a learning algorithm by gradient descent method is included in the fuzzy control system such that the membership functions are tuned and the necessary rules are created automatically for good control performance. For stable learning in gradient descent method, the optimal range of learning coefficient not to be trapped and not to provide too slow learning speed is investigated. With the optimal range of learning coefficient, the optimal value of learning coefficient is suggested and with this value, the gradient...eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectFuzzy on-line learning controller-
dc.subjectFuzzy off-line learning controller-
dc.subjectImproved learning algorithm-
dc.subjectNuclear steam generator level control-
dc.subject증기발생기수위제어-
dc.subject퍼지온라인학습제어기-
dc.subject퍼지오프라인학습제어기-
dc.subject개선된힉습알고리듬-
dc.titleDesign of fuzzy learning control systems for steam generator water level control = 증기발생기 수위제어를 위한 퍼지학습 제어시스템 개발에 관한 연구-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN108790/325007-
dc.description.department한국과학기술원 : 원자력공학과, -
dc.identifier.uid000925122-
dc.contributor.localauthorPark, Gee-Yong-
dc.contributor.localauthor박기용-
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NE-Theses_Ph.D.(박사논문)
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