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題名 The Learning-based Approach to Forecasting the Flight Load Factor
作者 Wei-Ting, Liang
Hsuan-Yun, Chang
Rua-Huan, Tsaih
貢獻者 2019智慧企業資訊應用發展國際研討會
關鍵詞 CSI learning, cramming mechanism, softening and integrating mechanism, least trimmed squares, flight load factor
日期 2019-06
上傳時間 17-Jul-2019 15:10:54 (UTC+8)
摘要 To effectively forecast the flight load factor is pivotal in the aviation industry. This study proposes the cramming, softening and integrating (CSI) algorithm, a sequentially-learning-based algorithm, to forecast the flight load factor. The proposed CSI learning algorithm has the following features: (1) the implementation of new data-driven algorithm using adaptive single-hidden layer feed-forward neural networks, (2) the usage of least trimmed squares principle to speed up the training time, (3) the practice of cramming mechanism to precisely learning all training data, and (4) the implementations of the regularization term and the softening and integrating mechanism to alleviate the obtained model from the overfitting pain. An experiment with real data from one of Taiwan aviation companies has been conducted to explore whether the proposed CSI learning algorithm can (1) predict the flight load factor better than other methods in the current literature, the Littlewood`s rule, (2) perfectly learn thorough training data, as it claims, and (3) alleviate the overfitting pain through the regularization term and the softening and integrating mechanism. The experimental results are promising.
關聯 2019智慧企業資訊應用發展國際研討會
資料類型 conference
dc.contributor 2019智慧企業資訊應用發展國際研討會
dc.creator (作者) Wei-Ting, Liang
dc.creator (作者) Hsuan-Yun, Chang
dc.creator (作者) Rua-Huan, Tsaih
dc.date (日期) 2019-06
dc.date.accessioned 17-Jul-2019 15:10:54 (UTC+8)-
dc.date.available 17-Jul-2019 15:10:54 (UTC+8)-
dc.date.issued (上傳時間) 17-Jul-2019 15:10:54 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124355-
dc.description.abstract (摘要) To effectively forecast the flight load factor is pivotal in the aviation industry. This study proposes the cramming, softening and integrating (CSI) algorithm, a sequentially-learning-based algorithm, to forecast the flight load factor. The proposed CSI learning algorithm has the following features: (1) the implementation of new data-driven algorithm using adaptive single-hidden layer feed-forward neural networks, (2) the usage of least trimmed squares principle to speed up the training time, (3) the practice of cramming mechanism to precisely learning all training data, and (4) the implementations of the regularization term and the softening and integrating mechanism to alleviate the obtained model from the overfitting pain. An experiment with real data from one of Taiwan aviation companies has been conducted to explore whether the proposed CSI learning algorithm can (1) predict the flight load factor better than other methods in the current literature, the Littlewood`s rule, (2) perfectly learn thorough training data, as it claims, and (3) alleviate the overfitting pain through the regularization term and the softening and integrating mechanism. The experimental results are promising.
dc.format.extent 40898 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) 2019智慧企業資訊應用發展國際研討會
dc.subject (關鍵詞) CSI learning, cramming mechanism, softening and integrating mechanism, least trimmed squares, flight load factor
dc.title (題名) The Learning-based Approach to Forecasting the Flight Load Factor
dc.type (資料類型) conference