Extraction operation know-how from historical operation data - Using characterization method of time series data and data mining method

被引：0
|
作者：
Takeda, K [1 ]
Tsuge, Y
Matsuyama, H
机构：
[1] Kyushu Univ, Dept Chem Engn, Fukuoka 8128581, Japan
[2] Waseda Univ, Grad Sch Informat Prod & Syst, Kitakyushu, Fukuoka 8080135, Japan
来源：
KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS | 2004年 / 3214卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In these days, it is very difficult to hand down experts' operation know-how to beginner, because of operation technique of a large and highly complex plant and reducing operators. On the other hand, data mining methods (See5, naive bayes, k-nearest neighbor, and so on) has been proposed as knowledge discovering methods from a huge amount of data. See5 outputs decision trees or IF-THEN rules as data mining results. However, See5 cannot recognize data as time series. In this study an extraction method of experts' operation know-how from historical operation data is proposed. Furthermore efficiencies of the proposed method are demonstrated by numerical experiments using a dynamic simulator.
引用
收藏
页码：375 / 381
页数：7
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