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RBF ネットワークによる多目的逐次近似最適化
https://doi.org/10.24517/00008101
https://doi.org/10.24517/00008101a11f8455-41c0-444a-9485-ccbf5026ed37
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-KITAYAMA-S-3476.pdf (2.4 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2017-10-03 | |||||||||||
| タイトル | ||||||||||||
| タイトル | RBF ネットワークによる多目的逐次近似最適化 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Sequential approximate multi-objective optimization using RBF network | |||||||||||
| 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 | 10.24517/00008101 | |||||||||||
| ID登録タイプ | JaLC | |||||||||||
| 著者 |
北山, 哲士
× 北山, 哲士× 荒川, 雅生× 山崎, 光悦 |
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| 著者別表示 |
Kitayama, Satoshi
× Kitayama, Satoshi
× Arakawa, Masao
× 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 772, p. 3476-3485, 発行日 2010-12-01 |
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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 | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | In this paper, a sequential approximate multi-objective optimization procedure by the Radial Basis Function (RBF) network with the Satisficing Trade-Off Method (STOM) is proposed. The sampling strategy is an important issue in the sequential approximate optimization. In this paper, the density function and the pareto fitness function are proposed. The objective of the density function is to find the sparse region in the design variable space. New samplings point are obtained by optimizing the density function. The objective of the pareto fitness function is to find the approximate set of pareto optimal solutions from the given data. New sampling point is obtained by optimizing the pareto fitness function. Both functions are constructed by the RBF network. By using both functions, the approximate set of pareto optimal solutions can be found effectively even when the set of pareto optimal solutions are separeted. Through simple numerical examples, the validity of proposed sampling strategy is examined. | |||||||||||
| 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 | |||||||||||
