한국지능시스템학회:학술대회논문집 (Proceedings of the Korean Institute of Intelligent Systems Conference)
- 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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- Pages.296-301
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- 1998
Temperature Inference System by Rough-Neuro-Fuzzy Network
- Il Hun jung (SAMSUNG ELECTRONICS CO.LTD) ;
- Park, Hae jin (SAMSUNG ELECTRONICS CO.LTD) ;
- Kang, Yun-Seok (SAMSUNG ELECTRONICS CO.LTD) ;
- Kim, Jae-In (SAMSUNG ELECTRONICS CO.LTD) ;
- Lee, Hong-Won (SAMSUNG ELECTRONICS CO.LTD) ;
- Jeon, Hong-Tae (Dept. of Electronic Engineering, Chung-Ang University)
- 발행 : 1998.06.01
초록
The Rough Set theory suggested by Pawlak in 1982 has been useful in AI, machine learning, knowledge acquisition, knowledge discovery from databases, expert system, inductive reasoning. etc. The main advantages of rough set are that it does not need any preliminary or additional information about data and reduce the superfluous informations. but it is a significant disadvantage in the real application that the inference result form is not the real control value but the divided disjoint interval attribute. In order to overcome this difficulty, we will propose approach in which Rough set theory and Neuro-fuzzy fusion are combined to obtain the optimal rule base from lots of input/output datum. These results are applied to the rule construction for infering the temperatures of refrigerator's specified points.