Abstract
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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Takeda, K., Tsuge, Y., Matsuyama, H. (2004). Extraction Operation Know-How from Historical Operation Data. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_48
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DOI: https://doi.org/10.1007/978-3-540-30133-2_48
Publisher Name: Springer, Berlin, Heidelberg
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