3A2 AN APPROACH USING GENETICS-BASED MACHINE LEARNING TO COMPLEMENTING LEGACY RULES FOR THE ELEVATOR OPERATION
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概要
- 論文の詳細を見る
In this paper, an approach using a Genetic-Based Machine Learning (GBML) method to acquire rule sets which complement legacy rules for the elevator operation is shown. A rule set is designed to be comprised of unit-rules, each of which encodes an elevator's augmented attribute vector and matches an arbitrary elevator which has such attributes. The GBML method is built in the Pittsburgh-style and tailored for acquiring such rules sets. In computer illustrations, the effectiveness of acquired rule sets is displayed as for three traffic patterns.
- 一般社団法人日本機械学会の論文
- 2009-07-04
著者
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Ohta Chikara
Graduate School Of Engineering Kobe University
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Inamoto Tsutomu
Graduate School of Engineering, Kobe University
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Tamaki Hisashi
Graduate School of Engineering, Kobe University
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Murao Hajime
Graduate School of Intercultural Studies, Kobe University
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Murao Hajime
Graduate School Of Intercultural Studies Kobe University
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Tamaki Hisashi
Graduate School Of Engineering Kobe University
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Inamoto Tsutomu
Graduate School Of Engineering Kobe University
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