# 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(機械力学,計測,自動制御)Sequential Approximate Optimization Using RBF Network : Basic Examination on the Sampling Function(Mechanical Systems)

## 抄録

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.

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.

## 収録刊行物

• 日本機械学会論文集 C編

日本機械学会論文集 C編 76(768), 1978-1987, 2010

日本機械学会 = The Japan Society of Mechanical Engineers

## 各種コード

• NII論文ID(NAID)
110007685473
• NII書誌ID(NCID)
AN00187463
• 本文言語コード
JPN
• 資料種別
ART
• ISSN
03875024
• NDL 記事登録ID
10812651
• NDL 雑誌分類
ZN11(科学技術--機械工学・工業)
• NDL 請求記号
Z16-1056
• データ提供元
CJP書誌  CJP引用  NDL  NII-ELS  IR  J-STAGE

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