Long-term prediction of industrial furnace by Extended Sequential Prediction method of LOM

Masatoshi Ogawa*, Yichun Yeh, Syou Kawanari, Harutoshi Ogai


研究成果: Conference contribution

2 被引用数 (Scopus)


Recently, attention has been drawn by the local modeling techniques of a new idea called "Just-In-Time (JIT) modeling" or "Lazy Learning". To apply "JIT modeling" to a large amount of database online, "Large-scale database-based Online Modeling (LOM)" has been proposed. LOM is such a technique that makes the retrieval of "neighboring" data more efficient by using "stepwise selection" and quantization. This paper reports an Extended Sequential Prediction (ESP) method of LOM with the local regression model. The ESP method is able to predict process variables over a long period by modeling the operator and the plant based on LOM, the approach is to repeat a process that predicts the process variables of the next step by using the predicted variables of the previous step. The method is applied to a dynamic industrial furnace with several deeply-intertwined physical phenomena; practical effectiveness of the method is verified. As a result, the method has predicted the process variables with satisfactory accuracy.

ホスト出版物のタイトルProceedings of SICE Annual Conference 2010, SICE 2010 - Final Program and Papers
出版社Society of Instrument and Control Engineers (SICE)
出版ステータスPublished - 2010 1月 1


名前Proceedings of the SICE Annual Conference

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学


「Long-term prediction of industrial furnace by Extended Sequential Prediction method of LOM」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。