書誌事項
- タイトル別名
-
- Active Mining from Process Time Series by Learning Classifier System
- ガクシュウ ブンルイシ システム オ モチイタ プロセス ジケイレツ ノ アクティブマイニング
この論文をさがす
抄録
Continuation processes in chemical and/or biotechnical plants always generate a large amount of time series data. However, since conventional process models are described as a set of control models, it is difficult to explain the complicated and active plant behaviors. Based on the background, this research proposes a novel method to develop a process response model from continuous time-series data.<P> The method consists of the following phases: 1) Collect continuous process data at each tag point in a target plant; 2) Normalize the data in the interval between zero and one; 3) Get the delay time, which maximizes the correlation between given two time series data; 4) Select tags with the higher correlation; 5) Develop a process response model to describe the relations among the process data using the delay time and the correlation values; 6) Develop a process prediction model via several tag points data using a neural network; 1) Discover control rules from the process prediction model using Learning Classifier system.<P> The main contribution of the research is to establish a method to mine a set of meaningful control rules from Learning Classifier System using the Minimal Description Length criteria. The proposed method has been applied to an actual process of a biochemical plant and has shown the validity and the effectiveness.
収録刊行物
-
- 人工知能学会論文誌
-
人工知能学会論文誌 17 638-646, 2002
一般社団法人 人工知能学会
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390001205106865152
-
- NII論文ID
- 10015771841
-
- NII書誌ID
- AA11579226
-
- ISSN
- 13468030
- 13460714
-
- NDL書誌ID
- 6449765
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可