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

Kazuhiro Takeda, Yoshifumu Tsuge, Hisayoshi Matsuyama

研究成果: Chapter in Book/Report/Conference proceedingChapter

抄録

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.

本文言語英語
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
編集者Mircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
出版社Springer Verlag
ページ375-381
ページ数7
ISBN(印刷版)9783540232063
DOI
出版ステータス出版済み - 2004
外部発表はい

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3214
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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