@inproceedings{b2aafb93305e4a478ad12ff4b976d0f8,
title = "An Improved Data-Driven Modeling Method for Aircraft Based on Prediction and Optimization",
abstract = "This paper proposes a data-driven modeling method for aircraft by predicting model errors and optimizing model structures. Based on flight test data from the mechanism model, the aircraft data-driven model is established by several trained basic neural networks for fitting dynamics relationships of aircraft and recurrent neural networks for compensating for model errors. Compared to the traditional data-driven modeling method, this method can more effectively avoid and solve the problem of instability of data-driven models with disturbances at long running times. Finally, the proposed method's feasibility and the established model's credibility are verified by simulation experiments with complex disturbance and statistical analysis for model accuracy.",
keywords = "aircraft, data-driven, disturbance, long running time, model prediction, structure optimization",
author = "Shihong Su and Bing Xiao and Lingwei Li and Jinfeng Luo and Hui Zhao",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",
year = "2023",
doi = "10.1109/CCDC58219.2023.10326645",
language = "英语",
series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2560--2565",
booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
}