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RBFネットワークによる逐次近似最適化 : サンプル関数の基礎的検討
https://doi.org/10.24517/00007899
https://doi.org/10.24517/000078992e3aa9d0-28a6-4543-b4e2-04f62920aea8
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-KITAYAMA-S-1978.pdf (550.9 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2017-10-03 | |||||||||
| タイトル | ||||||||||
| タイトル | RBFネットワークによる逐次近似最適化 : サンプル関数の基礎的検討 | |||||||||
| タイトル | ||||||||||
| タイトル | Sequential Approximate Optimization using RBF network (basic examination on the sampling function) | |||||||||
| 言語 | en | |||||||||
| 言語 | ||||||||||
| 言語 | jpn | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| ID登録 | ||||||||||
| ID登録 | 10.24517/00007899 | |||||||||
| ID登録タイプ | JaLC | |||||||||
| 著者 |
北山, 哲士
× 北山, 哲士× 山崎, 光悦 |
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| 著者別表示 |
Kitayama, Satoshi
× Kitayama, Satoshi
× Yamazaki, Koetsu
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| 提供者所属 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 金沢大学理工研究域機械工学系 | |||||||||
| 書誌情報 |
Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C 巻 76, 号 768, p. 1978-1987, 発行日 2010-08-01 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 0387-5024 | |||||||||
| NCID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00187463 | |||||||||
| 出版者 | ||||||||||
| 出版者 | 日本機械学会 = The Japan Society of Mechanical Engineers | |||||||||
| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | One of the important issues on the Sequential Approximate Optimization (SAO) is the sampling strategy. The sampling strategy for SAO using the Radial Basis Function (RBF) network is proposed in this paper. The proposed sampling strategy consists of three parts, which are called the density function, the boundary function, and random sampling. In order to add the new sampling points effectively, the density function and the boundary fuction are constructed by the RBF network. The objective of the density function is to find the sparse region in the design variable space and is to add the new sampling points in this region. In the constrained optimization problems, at least, one or more constraints will be active. As the result, it is desirable to add the new sampling points on the constratins. The objective of the boundary function is to add the new sampling points on the boundary. In addition, the random sampling is also introduced to spread the search region. The algorithm of proposed sampling strategy is described in detail. Through the numerical examples, the validity is examined. | |||||||||
| 権利 | ||||||||||
| 権利情報 | Copyright (c) 2010 日本機械学会 | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | VoR | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
| 関連URI | ||||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | http://www.jsme.or.jp/ | |||||||||
