| 作成者 |
|
|
|
|
|
|
|
|
|
|
|
| 本文言語 |
|
| 出版者 |
|
| 発行日 |
|
| 収録物名 |
|
| 巻 |
|
| 号 |
|
| 開始ページ |
|
| 終了ページ |
|
| 出版タイプ |
|
| アクセス権 |
|
| 権利関係 |
|
| 関連DOI |
|
| 関連URI |
|
| 関連HDL |
|
| 概要 |
Surrogate models can be used to replace expensive computer simulations for the purposes of optimization. In this paper, we propose an optimization approach based on artificial neural network (ANN) sur...rogate models and infill sampling criteria (ISC) strategy to evaluate design variables. The criterion for infill sample selection is a function which aims at identify design that offer potential improvement. We employ four widely used analytical benchmark problems to test the proposed approach. Our results show that a more accurate surrogate model obtained with fewer points is obtained when one includes the infill sample criterion to an ANN-based optimization.続きを見る
|
Mendeley出力
