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タイトル
和文: 
英文:Technology Extraction of Expert Operator Skills from Process Time Series Data 
著者
和文: 倉橋節也, 寺野 隆雄.  
英文: Setsuya Kurahashi, Takao Terano.  
言語 English 
掲載誌/書名
和文: 
英文:Lecture Notes in Computer Science 
巻, 号, ページ Vol. 4998        pp. 269-285
出版年月 2009年12月 
出版者
和文: 
英文:Springer Berlin / Heidelberg 
会議名称
和文: 
英文: 
開催地
和文: 
英文: 
DOI https://doi.org/10.1007/978-3-540-88138-4_16
アブストラクト 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 complicated and active plant behaviors. To uncover complex plant behaviors, this paper proposes a new method of developing a process response model from continuous time-series data. The method consists of the following phases: (1) Reciprocal correlation analysis; (2) Process response model; (3) Extraction of control rules; (4) Extraction of a workflow; and (5) Detection of outliers. The main contribution of the research is to establish a method to mine a set of meaningful control rules from a Learning Classifier System using the Minimum Description Length criteria and Tabu search method. The proposed method has been applied to an actual process of a biochemical plant and has shown its validity and effectiveness.

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