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.
|Number of pages||7|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - Dec 1 2004|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)