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Research on flight training prediction based on incremental online learning

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Abstract

With the continuous development of civil aviation industry in recent years, the demand for flight training has been increasing and the flight training safety requirements have been improving. To address the problem that the mining of flight training data is not deep enough and the research of flight training data is delayed, the study proposes a flight training data prediction model based on incremental learning to achieve real-time flight training data prediction and ensure flight training safety. The model firstly uses a small amount of data to generate a base model; secondly, on this base model, a specific amount of data is input to the model for learning according to a certain time frequency to continuously optimize the base model; finally, the model is compared with several models for experiments. The experimental results show that compared with the traditional model, the model has high prediction accuracy and good real-time performance in flight training data prediction, which can better ensure flight training safety.

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Funding

This research was supported by the Key Laboratory Research Project of Flight Technology and Flight Safety of Civil Aviation of China, Grant No. [FZ2020ZZ02]; the Safety Capabil-ity Project of Civil Aviation Administration of China, Grant No. [14002600100020J103]; and the Key Research and Development Project of Sichuan Province, China, Grant No. [2022YFG0027]. These funds will help further research on flight training safety issues and ensure flight training safety.

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Authors and Affiliations

  1. College of Computer Science and Technology, Civil Aviation Flight University of China, Section 4 of Nanchang Road, Guanghan, Sichuan, 618307, China

    Jing Lu, Yu Shi, Zhou Ren, Yitao Zhong, Yidan Bai & Jingli Deng

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Yudao Street, Nanjing, Jiangsu, 211106, China

    Jing Lu

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  1. Jing Lu
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  2. Yu Shi
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Contributions

Conceptualization, Y.S. and Z.R.; methodology, Y.S.; validation, Z.R.; formal analysis, Y.Z.; re-sources, J.U.; data curation, Y.B., J.D.PF; writing-original draft preparation, J.U.; writ-ing-review and editing, Y.S.; visualization, Z.R.; supervision, J.U.; project administration, J.U.; funding acquisition, J.U. All authors have read and agreed to the published version of the man-uscript.

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Correspondence to Jing Lu.

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Lu, J., Shi, Y., Ren, Z. et al. Research on flight training prediction based on incremental online learning. Appl Intell 53, 25662–25677 (2023). https://doi.org/10.1007/s10489-023-04930-9

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