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Modeling of the Visual Approach to Landing Using Neural Networks and Fuzzy Supervisory Control
http://hdl.handle.net/2261/28750
http://hdl.handle.net/2261/28750d9d26526-b3da-4981-84f5-ab9ee73b8033
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
JorgEntzinger_AerospaceScienceAndTechnology2009.pdf (2.0 MB)
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| Item type | 一般雑誌記事 / Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-01-17 | |||||
| タイトル | ||||||
| タイトル | Modeling of the Visual Approach to Landing Using Neural Networks and Fuzzy Supervisory Control | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Pilot modeling | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | aircraft landing | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | visual perception | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | neural networks | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | fuzzy logic | |||||
| 資源タイプ | ||||||
| 資源 | http://purl.org/coar/resource_type/c_6501 | |||||
| タイプ | article | |||||
| 著者 |
Entzinger, Jorg Onno
× Entzinger, Jorg Onno× Suzuki, Shinji |
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| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 3834 | |||||
| 姓名 | エントジンガー, ヨルグ オノ | |||||
| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 3835 | |||||
| 姓名 | 鈴木, 真二 | |||||
| 著者所属 | ||||||
| 著者所属 | 東京大学工学系研究科 航空宇宙工学専攻 | |||||
| 著者所属 | ||||||
| 著者所属 | Department of Aeronautics and Astronautics, University of Tokyo | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | During the visual approach to landing of a fixed wing aircraft, a human pilot bases his control and timing of subsequent maneuvers mainly on the out-the-window view, as there is not sufficient time to read out all instruments. The skill of making smooth and soft landings is acquired mainly through experience. Research has been done to identify the most important features in the visual scene (cues) for two phases of the visual approach to landing: glide slope tracking and the flare maneuver. Using simulator and real flight data, neural networks have been trained for both phases to mimic the pilot's control based on the visual cues available. By using the gamma-operator in neuron transfer functions, a transparent model is obtained. Fuzzy supervisory control is proposed to couple the networks and thus provide insight in the pilot's decision making process with respect to timing of the flare initiation. | |||||
| 書誌情報 |
Aerospace science and technology 発行日 2009-10 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 12709638 | |||||
| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA11136713 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | info:doi/10.1016/j.ast.2009.10.002 | |||||
| 権利 | ||||||
| 権利情報 | Copyright @ 2009 Elsevier Masson SAS All rights reserved | |||||
| 日本十進分類法 | ||||||
| 主題Scheme | NDC | |||||
| 主題 | 538 | |||||
| 出版者 | ||||||
| 出版者 | Elsevier | |||||
